1 Introduction

This report summarizes analysis of single-cell RNA-sequencing (scRNA-seq) data including single sample analysis, label transfer from a cell-type annotated reference (optional), integration with batch correction, differential expression with support for pseudobulk and multi-condition comparisons, and pathway analysis. All plots, tables, markers, Seurat objects, RISC object, DE results, and pathway analysis results are stored in the output folder.

The analysis pipelines in this report were developed by the lab of Dr. Deyou Zheng in the Department of Genetics and Department of Neuroscience at Albert Einstein College of Medicine. The pipeline was assembled primarily by PhD candidate Alexander Ferrena with additional input from all lab members.

1.1 Description of methods

Below is a short methods-section style description of the pipeline. More detailed information on can be found in each sub-section. If required, software versions of all R packages can be found at the bottom of the document in the SessionInfo section. The version of Cellranger can be found in the web_summary.html files provided with the data release. Please read carefully and adjust accordingly before using for manuscripts or applications.

text1 <- 
  '
Sequencing data from the 10x Chromium samples were first analyzed with Cellranger to generate cell level gene expression data [(10X Genomics)](https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger). Cells with fewer than 500 Unique Molecular Identifiers (UMIs) detected, fewer than 200 unique genes detected, more than 25% mitochondrial content, or more than 25% hemoglobin gene expression were filtered out (unless otherwise specified). An automated filtering approach to detect outliers of these variables based on median absolute deviation above 3 was also applied. Furthermore, complexity analysis based on the expected number of genes given number of UMIs per cell was also used to filter out poor quality cells. Samples were analyzed using the Seurat workflow [(Hao et al 2021)](https://doi.org/10.1016/j.cell.2021.04.048). Each sample was normalized using the SingleCellTransform pipeline [(Hafemeister & Satija 2019)](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1874-1) with method set to "glmGamPoi" [(Ahlmann-Eltze & Huber 2020)](https://academic.oup.com/bioinformatics/article/36/24/5701/6028985). 30 principal components were used for graph construction and resolution was set to 0.5 for Louvain clustering (unless otherwise specified). Marker analysis was performed using the Wilcoxon Rank Sum Test as implemented in the Seurat FindAllMarkers() function with "only.pos" set to True. To identify cell types present in each sample, the Seurat anchor-based Label Transfer method was used, based on a single-cell RNA-seq dataset from similar tissue (unless otherwise specified).


'


if(Pseudobulk_mode == T){
  
  text2 <- 'Samples were integrated using the RISC package [(Liu, Zheng et al Nat Biotech 2021)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8456427/). 30 PCs were used for integration as well as for post-clustering analysis including Louvain clustering, unless otherwise specified. Compositional analysis to compare proprotional abundance of clusters between conditions was performed via the Propeller test as implemented in the speckle package [(Phipson et al 2022)](https://academic.oup.com/bioinformatics/article/38/20/4720/6675456). Differential expression analysis across conditions followed a pseudobulking approach based on the EdgeR-Likelihood Ratio Test (LRT) method for each cluster [(Robinson et al 2010)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796818/). Pathway analysis on the differentially expressed genes was performed using Gene Set Enrichment Analysis (GSEA) as implemented in the FGSEA package [(Subramanian et al 2005)](https://www.pnas.org/doi/10.1073/pnas.0506580102), [(Korotkevich et al 2021)](https://www.biorxiv.org/content/10.1101/060012v3). Pathways were downloaded from the Molecular Signatures Database using the R package msigdbr [(Liberzon et al 2015)](https://academic.oup.com/bioinformatics/article/27/12/1739/257711), [(Dolgalev 2022)](https://github.com/igordot/msigdbr).
'
  
}
if(Pseudobulk_mode == F){
  
  text2 <- 'Samples were integrated using the RISC package [(Liu, Zheng et al Nat Biotech 2021)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8456427/). 30 PCs were used for integration as well as for post-clustering analysis including Louvain clustering, unless otherwise specified. Compositional analysis to compare proprotional abundance of clusters between conditions was performed via the 2-sample Z test as implemented in the R prop.test() function. Differential expression analysis across conditions used the Wilcoxon test as implemented in the Seurat FindMarkers() function. Pathway analysis on the differentially expressed genes was performed using Gene Set Enrichment Analysis (GSEA) as implemented in the FGSEA package [(Subramanian et al 2005)](https://www.pnas.org/doi/10.1073/pnas.0506580102), [(Korotkevich et al 2021)](https://www.biorxiv.org/content/10.1101/060012v3). Pathways were downloaded from the Molecular Signatures Database using the R package msigdbr [(Liberzon et al 2015)](https://academic.oup.com/bioinformatics/article/27/12/1739/257711), [(Dolgalev 2022)](https://github.com/igordot/msigdbr).
'
  
}


# cat(text1); cat(text2)

alltext <- paste0(text1, '\n', text2)
cat(alltext)

Sequencing data from the 10x Chromium samples were first analyzed with Cellranger to generate cell level gene expression data (10X Genomics). Cells with fewer than 500 Unique Molecular Identifiers (UMIs) detected, fewer than 200 unique genes detected, more than 25% mitochondrial content, or more than 25% hemoglobin gene expression were filtered out (unless otherwise specified). An automated filtering approach to detect outliers of these variables based on median absolute deviation above 3 was also applied. Furthermore, complexity analysis based on the expected number of genes given number of UMIs per cell was also used to filter out poor quality cells. Samples were analyzed using the Seurat workflow (Hao et al 2021). Each sample was normalized using the SingleCellTransform pipeline (Hafemeister & Satija 2019) with method set to “glmGamPoi” (Ahlmann-Eltze & Huber 2020). 30 principal components were used for graph construction and resolution was set to 0.5 for Louvain clustering (unless otherwise specified). Marker analysis was performed using the Wilcoxon Rank Sum Test as implemented in the Seurat FindAllMarkers() function with “only.pos” set to True. To identify cell types present in each sample, the Seurat anchor-based Label Transfer method was used, based on a single-cell RNA-seq dataset from similar tissue (unless otherwise specified).

Samples were integrated using the RISC package (Liu, Zheng et al Nat Biotech 2021). 30 PCs were used for integration as well as for post-clustering analysis including Louvain clustering, unless otherwise specified. Compositional analysis to compare proprotional abundance of clusters between conditions was performed via the Propeller test as implemented in the speckle package (Phipson et al 2022). Differential expression analysis across conditions followed a pseudobulking approach based on the EdgeR-Likelihood Ratio Test (LRT) method for each cluster (Robinson et al 2010). Pathway analysis on the differentially expressed genes was performed using Gene Set Enrichment Analysis (GSEA) as implemented in the FGSEA package (Subramanian et al 2005), (Korotkevich et al 2021). Pathways were downloaded from the Molecular Signatures Database using the R package msigdbr (Liberzon et al 2015), (Dolgalev 2022).

1.2 Pipeline Parameters

plotlab <- '

### Sample names and conditions

Here we list the sample names, conditions, and code names for each sample.


'

cat(plotlab)

1.2.1 Sample names and conditions

Here we list the sample names, conditions, and code names for each sample.

knitr::kable(sample_metadata)

Sample	Condition	Code
H00049	Healthy	Healthy_1
H00053	Healthy	Healthy_2
S00006	Covid_Mild	Covid_Mild_1
S00016	Covid_Mild	Covid_Mild_2
S00040	Covid_Critical	Covid_Critical_1
S00043	Covid_Critical	Covid_Critical_2

plotlab <- '

### Comparisons to perform

We set up a cross-condition comparison using each row of the table below. For example, for each row, the "c1" column is compared with the "c2" column in differential expression analysis.



'

cat(plotlab)

1.2.2 Comparisons to perform

We set up a cross-condition comparison using each row of the table below. For example, for each row, the “c1” column is compared with the “c2” column in differential expression analysis.

knitr::kable(comps)

c1	c2
Covid_Critical	Healthy
Covid_Mild	Healthy
Covid_Critical	Covid_Mild

plotlab <- '

### Analysis parameters

Here we list the key analysis parameters used in this analysis.



'

cat(plotlab)

1.2.3 Analysis parameters

Here we list the key analysis parameters used in this analysis.

rownames(pldf) <- NULL
knitr::kable(pldf)

parameter	value
datadir	/gs/gsfs0/users/aferrena/data/deyou/scDAPP/data//datadir//
outdir	/gs/gsfs0/users/aferrena/data/deyou/scDAPP/data//outs/PreRunOut//
sample_metadata	/gs/gsfs0/users/aferrena/data/deyou/scDAPP/data//sample_metadata.csv
comps	/gs/gsfs0/users/aferrena/data/deyou/scDAPP/data//comps.csv
use_labeltransfer	TRUE
refdatapath	/gs/gsfs0/users/aferrena/data/deyou/scDAPP/data//labeltransferref/LabelTransferRef_SCTnormalized.rds
m_reference	/gs/gsfs0/users/aferrena/data/deyou/scDAPP/data//labeltransferref/LabelTransferRefMarkers.rds
min_num_UMI	500
min_num_Feature	200
max_perc_mito	25
max_perc_hemoglobin	25
autofilter_mito	TRUE
autofilter_nUMI	TRUE
autofilter_complexity	TRUE
autofilter_medianabsolutedev_threshold	3
autofilter_loess_negative_residual_threshold	-5
doubletFinder	TRUE
risc_reference	auto
pcs_indi	30
res_indi	0.5
pcs_int	30
res_int	0.5
RISC_louvain_neighbors	10
Pseudobulk_mode	TRUE
crossconditionDE_padj_thres	0.1
crossconditionDE_lfc_thres	0
pathway_padj_thres	0.1
species	Homo sapiens
workernum	6
input_seurat_obj	TRUE

2 Individual sample processing and analysis

First, we preprocess and analyze each individual sample. This will go through the following steps for each sample:

Preprocessing: Normalize and pre-process with the Seurat SingleCellTransform (SCT) pipeline
Dimension reduction and clustering: Principal component analysis, graph construction, clustering, and visualization via UMAP to discover clusters in each sample
Marker analysis: using the Wilcoxon test for differential expression, find unique marker genes of each cluster
Label transfer: from a reference single-cell dataset, quantify how much each cell in the data resembles a known cell type

After the analysis of individual samples, we will do an integrated analysis.

### load packages, set seed

library(tidyverse)
library(patchwork)  # combine plots
library(RISC)
library(Seurat)
library(scDAPP)
library(DoubletFinder)

library(future)
library(parallel)
library(foreach)

library(glmGamPoi)  # for faster SCT
library(ComplexHeatmap) # for heatmaps
library(ggdendro)       #for clustering dendrograms

library(ggridges) # qc ridgeplots

library(edgeR)

library(msigdbr)          #get pathways (cross species) from msigdb

library(hdf5r) # HARD TO INSTALL: installed thru mamba
library(ggalluvial) # part of alluvial plot
library(ggfittext) # part of alluvial plot
library(ggrepel) # part of alluvial plot


set.seed(2022)

3 Data Quality Assessment

Once sequencing is completed, we get a file that has the barcode and cDNA sequences as reads (a bunch of ATGCs), along with some sequencing quality information. This file is called a “FASTQ” and has the file extension “.fastq”. The downstream analysis relies on analyzing counts of transcripts for each cell. This means we have to do a few things:

Alignment: assign each sequence read to the gene it came from.
Gene demultiplexing: Once the read-pair is aligned to a gene, we count the UMI barcode as a count for that gene. Two reads may come from the same gene, but if they have the same UMI, it means they came from the same transcript, so it only counts at one UMI. This allows us to get around the problem of short-read sequencing inherent to Illumina technology, to count actual transcripts rather than counting reads.
cell demultiplexing: During 10X library prep, each cell is isolated and given a barcode. Every read from that cell is labelled with the cell barcode. Using the barcode, we assign the read to a specific cell.

These preprocessing steps are performed by the 10x Genomics software called Cellranger. Cellranger produces some outputs including web_summary.html files that allow basic exploration of the sample quality. It is good practice to review these files for each sample.

Downstream analysis, includng clustering and label transfer, works with the output of Cellranger Count. This includes a gene by cell matrix. Each row is a gene, each column is a cell and each value is the number of UMIs for each gene from each cell; typically, this is ~20K genes (rows) x ~8K cells (columns). Once that is all complete, we are ready for the analysis performed in this document. We use the Seurat pipeline in R to analyze this data, which involves the steps detailed in section 1.

Flawed datapoints can include poor quality cells and multiplets. Poor quality cells can include cells which have sustained membrane damage and leakage of their RNA, a process which typically results the observed transcriptomic profile of that cell displaying a high proportion of mitochondrial RNA, or low numbers of UMIs or unique genes. Multiplets can be caused by random error of poisson loading of single-cells in the 10X microfluidics chamber. These can influence and bias downstream analysis. Occasionally, we also sometimes observe red blood cells despite usage of RBC lysis buffer. These should not be in the data and so we remove them as well.

#set up output dir for individual samples
outdir_indi <- paste0(outdir, '/individualsample_analysis')

dir.create(outdir_indi)

### read in data ###



### use pseudobulk_md for good sample order
samples <- sample_metadata$Sample
names(samples) <- sample_metadata$Code



#read in data from filtered_feature_bc_matrix.h5 files or from Seurat object (ie for hashed)

if(input_seurat_obj == T){## read in
  sobjlist <- lapply(samples, function(samp){
    message('\nReading in ', samp)
    
    
    #for HTO, we will assume saved objects
    sobjfile <- paste0(datadir, '/', samp, '.rds')
    sobj <- readRDS(sobjfile)
    
    
    
    
    #make project name the Conditon_Sample
    md_samp <- sample_metadata[sample_metadata$Sample==samp,,drop=F]
    
    #make project name the sample
    sobj@project.name <- md_samp$Code
    
    #make orig.ident the code
    sobj$orig.ident <- md_samp$Code
    
    #return seurat obj
    sobj
    
    
  })
} else{
  
  ## read in
  sobjlist <- lapply(samples, function(samp){
    message('\nReading in ', samp)
    
    datafp <- paste0(datadir, '/', samp)
    # if on hpc, use below
    # datafp <- paste0(datadir, '/', samp, '/outs/')
    
    # for the dl data, we need to find the filepath
    h5_filename <- grep(pattern = 'filtered_feature_bc_matrix.h5',
                        list.files(datafp, recursive = T, full.names = T),
                        value = T)
    
    
    
    
    
    #read in
    sobj <- CreateSeuratObject(   Read10X_h5(h5_filename), 
                                  min.cells= 3)
    
    
    
    
    #make project name the Conditon_Sample
    md_samp <- sample_metadata[sample_metadata$Sample==samp,,drop=F]
    
    #make project name the sample
    sobj@project.name <- md_samp$Code
    
    #make orig.ident the code
    sobj$orig.ident <- md_samp$Code
    
    #return seurat obj
    sobj
    
    
  })
  
}

#add in qc values for mito and hemoglobin

sobjlist <- lapply(sobjlist, function(sobj){
  #mito content, add to metadata
  mito.features <- grep(pattern = "^mt-", x = rownames(x = sobj), value = TRUE, ignore.case = T)
  sobj[["percent.mito"]] <- Seurat::PercentageFeatureSet(sobj, features = mito.features)
  
  #hemoglobin content, add to metadata
  sobj$percent.hemoglobin <- scDAPP::calculate_percent.hemoglobin(sobj)
  
  #calculate phase (if possible, sometimes it fails)
  try(
    sobj <- CellCycleScoring(sobj,
                             s.features = Seurat::cc.genes.updated.2019$s.genes,
                             g2m.features = Seurat::cc.genes.updated.2019$g2m.genes)
  )
  
  sobj
  
})



#here we do most of the filtering 

rawobjsdir <- paste0(outdir_indi, '/unfiltered_Seurat_objects')
dir.create(rawobjsdir, recursive = T)

outdir_indi_seuratobjs <- paste0(outdir_indi, '/processed_Seurat_objects')
dir.create(outdir_indi_seuratobjs, recursive = T)

qcdir <- paste0(outdir_indi, '/qualitycontrol_filtering')
dir.create(qcdir, recursive = T)


#make a temp dir and run one at a time or parallel with foreach...
## remove seurat objects; save to tmp files instead...
qctmpdir <- paste0(qcdir, '/qctmpdir/')
dir.create(qctmpdir, recursive = T)
sobjlist <- lapply(sobjlist, function(sobj){
  code <- sobj@project.name
  tmpsobjfp <- paste0(qctmpdir, '/', code, '.rds')
  saveRDS(sobj, tmpsobjfp)
  return(sobj)
})


#clean up env

rm(sobjlist)

invisible(gc(full = T, reset = F, verbose = F))

#actual processing steps

#0. read in each sample from temp
#1. normalize and cluster raw data w/o filter
#2. apply autofilter
#3. filter out initial auto filter
#4. IF DF == T: renormalize, recluster, apply doubletfinder, refilter
#5. renormalize, recluster with real clustering parameters
#6. add all filter out annotation to raw sobj
#7. do some analysis on raw and save it
#8. save files (raw and filtered/processed)
#9. return autofilter results

cl <- parallel::makeCluster(workernum)
doParallel::registerDoParallel(cl)


codes <- sample_metadata$Code
# codes <- codes[11:17] troubleshoot particular samples



# af_md_list <- lapply(codes, function(code){
af_md_list <- foreach(code = sample_metadata$Code,
                      .packages = c('Seurat', 'ggplot2', 'dplyr',
                                    'scDAPP','grid', 'irlba', 'Matrix'), .verbose = T) %dopar%
  {
    
    
    message(code)
    
    #read in sobj 
    sobj <- readRDS(paste0(qctmpdir, '/', code, '.rds'))
    
    
    #1. normalize and cluster raw data w/o filter
    
    #normalize and cluster
    suppressWarnings(sobj <- Seurat::SCTransform(sobj, verbose = T, method="glmGamPoi"))
    
    sobj <- Seurat::RunPCA(object = sobj, verbose = F)
    
    sobj <- Seurat::FindNeighbors(object = sobj, dims = 1:30, verbose = F)
    sobj <- Seurat::FindClusters(object = sobj, resolution = 0.1, verbose = F, algorithm = 1)
    
    sobj <- Seurat::RunUMAP(sobj, dims = 1:30)
    
    
    
    
    #2. apply autofilter 
    af <- scDAPP::autofilter(sobj, 
                           min_num_UMI = min_num_UMI,
                           min_num_Feature = min_num_Feature,
                           max_perc_mito = max_perc_mito,
                           max_perc_hemoglobin = max_perc_hemoglobin,
                           globalfilter.complexity = autofilter_complexity,
                           globalfilter.mito = autofilter_mito,
                           globalfilter.libsize = autofilter_nUMI,
                           mad.score.threshold = autofilter_medianabsolutedev_threshold,
                           loess_negative_residual_threshold = autofilter_loess_negative_residual_threshold
    )
    
    
    
    
    
    #3. filter out initial auto filter 
    
    #name unfiltered object as sobjraw, and filtered as sobj for now and sobjsave after
    
    sobjraw <- sobj
    
    
    cellstatus <- af$cellstatus
    
    goodcells <- cellstatus[cellstatus$filteredout==F,"barcodes"]
    
    sobj <- sobj[,goodcells]
    
    
    
    
    #4. IF DF == T: renormalize, recluster, apply doubletfinder, refilter
    
    if(doubletFinder == T){
      #filter, re-proc
      
      
      ### temporary fix for doubletfinder v2.0.3 and Seurat v5: coerce seurat to "v3" instead of v5 object
      if( (packageVersion('DoubletFinder') == "2.0.3") & (packageVersion('Seurat') >= "5.0.0") ){
        
        ## try to make it a old seurat object...
        sobj_df <- GetAssayData(sobj, assay = 'SCT', layer = 'data')
        sobj_df <- CreateAssayObject(sobj_df)
        sobj_df <- CreateSeuratObject(sobj_df)
        
        warning('Will attempt to coerce v5 Seurat object to work with DoubletFinder v2.0.3; this is unstable and does not always work! If any errors arise, set doubletFinder to FALSE in pipeline runner')
        
      } else{sobj_df <- sobj}
      
      
      #normalize and cluster
      suppressWarnings(sobj_df <- Seurat::SCTransform(sobj_df, verbose = T, method="glmGamPoi"))
      
      sobj_df <- Seurat::RunPCA(object = sobj_df, verbose = F)
      
      sobj_df <- Seurat::FindNeighbors(object = sobj_df, dims = 1:30, verbose = F)
      sobj_df <- Seurat::FindClusters(object = sobj_df, resolution = 0.1, verbose = F, algorithm = 1)
      
      sobj_df <- RunUMAP(sobj_df, dims = 1:30)
      
      
      try(expr = {
        ### run DF
        af <- scDAPP::doubletfinderwrapper(sobj_df, 
                                         autofilterres = af, 
                                         num.cores = 1)
        
        
        
        
        #filter, re-proc
        cellstatus <- af$cellstatus
        
        goodcells <- cellstatus[cellstatus$filteredout==F,"barcodes"]
        
        sobj <- sobj[,goodcells]
      })
      
      rm(sobj_df); gc(full = T)
      
      
      
      
    }
    
    
    
    
    
    
    #previously we did main analysis here, for ease of code reading
    # we'll do it later in the clustering section
    
    # MAKE SURE RAW PREFILT CLUSTERS ARE LABELLED APPROPRIATELY IN SOBJSAVE 
    
    colnames(sobj@meta.data)[grepl('SCT_snn_res.0.1', colnames(sobj@meta.data))] <- 'PREFILTER_SCT_snn_res.0.1'
    
    #name filtered object as sobjsave
    sobjsave <- sobj ; rm(sobj)
    
    
    
    #7. do some analysis on raw and save it
    
    #do a bit of analysis
    #add af cell status to sobjraw md
    sobjraw@meta.data <- cbind(sobjraw@meta.data, af$cellstatus[,-1])
    
    #find markers; do not futurize, it breaks everything
    
    m <- FindAllMarkers(sobjraw, only.pos = T)
    # as of Nov 9 2023 (Seurat v5): add score to markers
    m$score <- (m$pct.1 - m$pct.2) * m$avg_log2FC
    
    
    #prep genes
    n <- 5
    top <- m %>% group_by(cluster) %>% top_n(n = n, wt = score)
    
    #make some plots
    d_rawclust <- DimPlot(sobjraw, group.by = 'seurat_clusters', label = T, repel = T)+ggtitle('Unfiltered data clusters', subtitle = 'Louvain res = 0.1')
    
    d_raw_filt <- DimPlot(sobjraw, group.by = 'filteredout', label = F, repel = T)
    
    
    sobjraw$filterreason <- factor(sobjraw$filterreason, levels = names(sort(table(sobjraw$filterreason), decreasing = T)))
    d_raw_filt_reason <- DimPlot(sobjraw, group.by = 'filterreason', label = F, repel = T)
    
    fp_raw_qc <- FeaturePlot(sobjraw, c('nCount_RNA', 'nFeature_RNA', 
                                        'percent.mito', 'percent.hemoglobin'),
                             order = T)
    
    #prep per-cluster filter numbers
    tab_filt_by_clust <- table(sobjraw$filterreason, sobjraw$seurat_clusters)
    tab_filt_by_clust <- t(tab_filt_by_clust)
    rownames(tab_filt_by_clust) <- paste0('cluster_', rownames(tab_filt_by_clust))
    colnames(tab_filt_by_clust) <- gsub(x = colnames(tab_filt_by_clust),
                                        pattern = '\\.', '\n')
    
    
    
    hm_raw <- DoHeatmap(sobjraw, top$gene, raster = F)+NoLegend() + labs(title = "Pre-filter cluster markers")
    
    # d_filt_clust <- DimPlot(sobjraw, group.by = newclustname, label = T, repel = T)+ggtitle('Filtered data clusters')
    
    
    #alluvial plot: prep colors and make sure order is hi to lo
    sobjraw$filterreason <- factor(sobjraw$filterreason, levels = names(sort(table(sobjraw$filterreason), decreasing = T)))
    pal <- grDevices::colorRampPalette(RColorBrewer::brewer.pal('Dark2', n = 8))(length(levels(sobjraw$seurat_clusters))) 
    
    
    ap_filt <- alluvialplot(sobjraw@meta.data[,c('seurat_clusters', 'filteredout')])+
      scale_fill_manual(values = pal)+
      labs(title = 'Cluster filtering')
    
    ap_filt_reason <- alluvialplot(sobjraw@meta.data[,c('seurat_clusters', 'filterreason')])+
      scale_fill_manual(values = pal)+
      labs(title = 'Cluster filtering reason')
    
    
    #add some basic filter vln plots
    comm <- af$allcommands
    rownames(comm) <- comm$Command
    
    af$vln_umi <- VlnPlot(sobjraw, 'nCount_RNA', group.by = 'orig.ident')+
      scale_y_log10(labels = scales::label_comma())+
      geom_hline(yintercept = comm['min_num_UMI', 2], 
                 linetype = 'dotted')+
      labs(caption = paste0("cutoff = ", comm['min_num_UMI', 2]))
    
    af$vln_feature <- VlnPlot(sobjraw, 'nFeature_RNA', group.by = 'orig.ident')+
      scale_y_log10(labels = scales::label_comma())+
      geom_hline(yintercept = comm['min_num_Feature', 2], 
                 linetype = 'dotted')+
      labs(caption = paste0("cutoff = ", comm['min_num_Feature', 2]))
    
    
    af$vln_mito <- VlnPlot(sobjraw, 'percent.mito', group.by = 'orig.ident')+
      geom_hline(yintercept = comm['max_perc_mito', 2], 
                 linetype = 'dotted')+
      labs(caption = paste0("cutoff = ", comm['max_perc_mito', 2]))
    
    af$vln_hemo  <- VlnPlot(sobjraw, 'percent.hemoglobin', group.by = 'orig.ident')+
      geom_hline(yintercept = comm['max_perc_hemoglobin', 2], 
                 linetype = 'dotted')+
      labs(caption = paste0("cutoff = ", comm['max_perc_hemoglobin', 2]))
    
    
    #add to autofilter
    af$d_rawclust <- d_rawclust
    af$d_raw_filt <- d_raw_filt
    af$d_raw_filt_reason <- d_raw_filt_reason
    
    af$fp_raw_qc <- fp_raw_qc
    
    af$tab_filt_by_clust <- tab_filt_by_clust
    
    af$hm_raw <- hm_raw
    #af$d_filt_clust <- d_filt_clust
    
    
    af$ap_filt <- ap_filt
    af$ap_filt_reason <- ap_filt_reason
    
    
    #change colnames for baseline sumary
    colnames(af$baseline_qc_summary) <- gsub("summary_", "summary\n", colnames(af$baseline_qc_summary))
    
    
    
    #save it all
    
    
    # save the raw objects
    saveRDS(sobjraw, paste0(rawobjsdir, '/Unfiltered-SeuratObject-', code, '.rds'))
    
    #save the autofilter as a nice pdf
    afpdf <- paste0(qcdir, '/QC_autofilter_summary-', code,'.pdf')
    
    pdf(afpdf, height = 10, width = 10)
    
    
    
    pdftable(af$filtersummary, title = 'Cell Filtering Summary')
    
    pdftable(af$allcommands, title = 'Filter parameters')
    
    pdftable(round(af$baseline_qc_summary, 2), title = 'QC summary stats')
    
    
    
    
    print(af$vln_umi)
    print(af$vln_feature)
    print(af$vln_mito)
    print(af$vln_hemo)
    
    print(af$globalfilter.complexity)
    print(af$globalfilter.libsize)
    print(af$globalfilter.mito)
    
    
    print(af$d_rawclust)
    print(af$d_raw_filt)
    print(af$d_raw_filt_reason)
    print(af$fp_raw_qc)
    
    
    pdftable(af$tab_filt_by_clust, title = 'Cell filtering per cluster')
    
    print(af$hm_raw)
    
    # print(af$d_filt_clust)
    
    print(af$ap_filt)
    print(af$ap_filt_reason)
    
    
    
    
    dev.off()
    
    
    #8. save procesed object
    #code <- sobj@project.name
    sobjfile <- paste0(qctmpdir, '/', code, '.rds')
    
    saveRDS(sobjsave, sobjfile)
    
    
    #9. return autofilter and raw md
    rawmd <- sobjraw@meta.data
    rm(sobjsave, sobjraw)
    invisible(gc(full = T, reset = F, verbose = F))
    
    
    list(af, rawmd)
    
    
  } # for foreach
# }) # for lapply



parallel::stopCluster(cl)


#recover the autofilter and metadata lists
aflist <- lapply(af_md_list, function(subl){
  subl[[1]]
})

mdlist <-  lapply(af_md_list, function(subl){
  subl[[2]]
})

names(aflist) <- sample_metadata$Code
names(mdlist) <- sample_metadata$Code

#clean mem
rm(af_md_list)
invisible(gc(full = T, reset = F, verbose = F))

### prepare some summary plots for filtering


# #from autofilter list, get baseline summaries and cell filter stats
cn <- colnames(aflist[[1]]$baseline_qc_summary)

bsl <- lapply(cn, function(var){
  
  sampsum <- lapply(1:length(aflist), function(i){
    
    af <- aflist[[i]]
    samp <- names(aflist)[i]
    sampsum <- af$baseline_qc_summary[,var,drop = F]
    colnames(sampsum) <- samp
    
    sampsum
    
    
  })
  
  sampsum <- dplyr::bind_cols(sampsum)
  sampsum <- t(sampsum)
  
})

cn <- gsub(x=cn, 'summary\n', '')
names(bsl) <- cn



#from outlier list, filter summary
fs <- lapply(1:length(aflist), function(i){
  af <- aflist[[i]]
  samp = names(aflist)[i]
  sum <- af$filtersummary
  rownames(sum) <- sum[,1]
  sum <- sum[,2, drop = F]
  colnames(sum) <- samp
  sum
})

#sometimes, some samples don't have any removed for some category ,so need to fix
remnames <- unique(unlist(lapply(fs, rownames)))

fs <- lapply(fs, function(sum){
  
  #identify missing filters and set to 0
  if( any(!(remnames %in% rownames(sum))) ){
    missingfilts <- remnames[!(remnames %in% rownames(sum))]
    adddf <- data.frame(samp = rep(0, length(missingfilts)))
    colnames(adddf) <- colnames(sum)
    rownames(adddf) <- missingfilts
    sum <- rbind(sum, adddf)
  }
  
  
  #make sure all have same order
  sum <- sum[match(remnames, rownames(sum)),,drop=F]
  
})


#bind table
fs <- dplyr::bind_cols(fs)
fs <- t(fs)

#shorten names
colnames(fs) <- gsub('globalfilter', 'auto', colnames(fs))
colnames(fs) <- gsub('DoubletFinder_doublet', 'Doublet', colnames(fs))

#rearragne, total cells, unfilt, then filtered cells
fs <- cbind( fs[,c(ncol(fs), ncol(fs)-1)], fs[,1:(ncol(fs)-2)] )


## also prep some summary plots for each sample ##

### pre-filt plots

combmd <- dplyr::bind_rows(mdlist)


umilims <- c(min(combmd$nCount_RNA), max(combmd$nCount_RNA)) 
featlims <- c(min(combmd$nFeature_RNA), max(combmd$nFeature_RNA))

### ridgeplots / ridgeline density plots


# #density plot for nCount_RNA
var <- "nCount_RNA"
submd <- combmd[,c(var,'orig.ident')]
colnames(submd)[1] <- 'var'
submd$var <- log10(submd$var)
maxdens <- aggregate(var ~ orig.ident, submd, function(x){max(density(x)$y)})

# submd <- combmd[,c(var,'orig.ident')]
# colnames(submd)[1] <- 'var'
# repelcoords <- aggregate(var ~ orig.ident, submd, median)
# repelcoords$maxdens <- maxdens$var
# 
# dens_UMI <- ggplot(combmd, aes(x = .data[[var]], col = orig.ident))+
#   geom_density()+
#   ggrepel::geom_text_repel(inherit.aes = F,
#                            data = repelcoords,
#                            aes(x = var, y = maxdens, label = orig.ident, color = orig.ident))+
#   scale_x_log10(labels = scales::label_comma(), name = var)

dens_UMI <- ggplot(combmd, aes(x = .data[[var]], y=orig.ident, fill=after_stat(log10(x))))+
  geom_density_ridges_gradient(scale=0.9, stat = "binline", bins = 50) +
  scale_x_log10(labels = scales::label_comma(), name = var, limits = umilims)+
  scale_y_discrete(limits = rev(sample_metadata$Code))+
  viridis::scale_fill_viridis( option = "C", alpha = 0.7) +
  theme_ridges()+theme(legend.position = 'none')

# 
# 
# #density plot for nFeature_RNA
var <- "nFeature_RNA"
submd <- combmd[,c(var,'orig.ident')]
colnames(submd)[1] <- 'var'
submd$var <- log10(submd$var)
maxdens <- aggregate(var ~ orig.ident, submd, function(x){max(density(x)$y)})

# submd <- combmd[,c(var,'orig.ident')]
# colnames(submd)[1] <- 'var'
# repelcoords <- aggregate(var ~ orig.ident, submd, median)
# repelcoords$maxdens <- maxdens$var
# 
# dens_feature <- ggplot(combmd, aes(x = .data[[var]], col = orig.ident))+
#   geom_density()+
#   ggrepel::geom_text_repel(inherit.aes = F,
#                            data = repelcoords,
#                            aes(x = var, y = maxdens, label = orig.ident, color = orig.ident))+
#   scale_x_log10(labels = scales::label_comma(), name = var)


dens_feature <- ggplot(combmd, aes(x = .data[[var]], y=orig.ident, fill=after_stat(log10(x))))+
  geom_density_ridges_gradient(scale=0.9, stat = "binline", bins = 50) +
  scale_x_log10(labels = scales::label_comma(), name = var, limits = featlims)+
  scale_y_discrete(limits = rev(sample_metadata$Code))+
  viridis::scale_fill_viridis( option = "C", alpha = 0.7) +
  theme_ridges()+theme(legend.position = 'none')
# 
# 
# #density plot for mito
var <- "percent.mito"
submd <- combmd[,c(var,'orig.ident')]
colnames(submd)[1] <- 'var'
maxdens <- aggregate(var ~ orig.ident, submd, function(x){max(density(x)$y)})

# submd <- combmd[,c(var,'orig.ident')]
# colnames(submd)[1] <- 'var'
# repelcoords <- aggregate(var ~ orig.ident, submd, median)
# repelcoords$maxdens <- maxdens$var
# 
# dens_mito <- ggplot(combmd, aes(x = .data[[var]]+0.1, col = orig.ident))+
#   geom_density()+
#   ggrepel::geom_text_repel(inherit.aes = F,
#                            data = repelcoords,
#                            aes(x = var, y = maxdens, label = orig.ident, color = orig.ident))+
#   scale_y_continuous(name = 'density')


dens_mito <- ggplot(combmd, aes(x = .data[[var]]+1, y=orig.ident, fill=after_stat(log10(x))))+
  geom_density_ridges_gradient(scale=0.9, stat = "binline", bins = 50) +
  scale_x_log10(labels = scales::label_comma(), name = var, limits = c(1,100))+
  scale_y_discrete(limits = rev(sample_metadata$Code))+
  viridis::scale_fill_viridis( option = "C", alpha = 0.7) +
  theme_ridges()+theme(legend.position = 'none')
# 
# 
# #density plot for hemoglobin
var <- "percent.hemoglobin"
submd <- combmd[,c(var,'orig.ident')]
colnames(submd)[1] <- 'var'
maxdens <- aggregate(var ~ orig.ident, submd, function(x){max(density(x)$y)})

# submd <- combmd[,c(var,'orig.ident')]
# colnames(submd)[1] <- 'var'
# repelcoords <- aggregate(var ~ orig.ident, submd, median)
# repelcoords$maxdens <- maxdens$var
# 
# dens_hemo <- ggplot(combmd, aes(x = .data[[var]]+0.01, col = orig.ident))+
#   geom_density()+
#   ggrepel::geom_text_repel(inherit.aes = F,
#                            data = repelcoords,
#                            aes(x = var, y = maxdens, label = orig.ident, color = orig.ident))+
#   scale_y_continuous(name = 'density')

dens_hemo <- ggplot(combmd, aes(x = .data[[var]]+1, y=orig.ident, fill=after_stat(log10(x))))+
  geom_density_ridges_gradient(scale=0.9, stat = "binline", bins = 50) +
  scale_x_log10(labels = scales::label_comma(), name = var,limits = c(1,100))+
  scale_y_discrete(limits = rev(sample_metadata$Code))+
  viridis::scale_fill_viridis( option = "C", alpha = 0.7) +
  theme_ridges()+theme(legend.position = 'none')




## also prep some summary plots for each sample ##

### post-fld plots

combmd <- dplyr::bind_rows(mdlist)

combmd <- combmd[combmd$filteredout == F,]

### ridgeplots / ridgeline density plots

# #density plot for nCount_RNA
var <- "nCount_RNA"

post_dens_UMI <- ggplot(combmd, aes(x = .data[[var]], y=orig.ident, fill=after_stat(log10(x))))+
  geom_density_ridges_gradient(scale=0.9, stat = "binline", bins = 50) +
  scale_x_log10(labels = scales::label_comma(), name = var,
                limits = umilims )+
  scale_y_discrete(limits = rev(sample_metadata$Code))+
  viridis::scale_fill_viridis( option = "C", alpha = 0.7) +
  theme_ridges()+theme(legend.position = 'none')


# #density plot for nFeature_RNA
var <- "nFeature_RNA"


post_dens_feature <- ggplot(combmd, aes(x = .data[[var]], y=orig.ident, fill=after_stat(log10(x))) )+
  geom_density_ridges_gradient(scale=0.9, stat = "binline", bins = 50) +
  scale_x_log10(labels = scales::label_comma(), name = var,
                limits = featlims )+
  scale_y_discrete(limits = rev(sample_metadata$Code))+
  viridis::scale_fill_viridis( option = "C", alpha = 0.7) +
  theme_ridges()+theme(legend.position = 'none')

# #density plot for mito
var <- "percent.mito"

post_dens_mito <- ggplot(combmd, aes(x = .data[[var]]+1, y=orig.ident, fill=after_stat(log10(x))))+
  geom_density_ridges_gradient(scale=0.9, stat = "binline", bins = 50) +
  scale_x_log10(labels = scales::label_comma(), name = var,
                limits = c(1,100) )+
  scale_y_discrete(limits = rev(sample_metadata$Code))+
  viridis::scale_fill_viridis( option = "C", alpha = 0.7) +
  theme_ridges()+theme(legend.position = 'none')


# #density plot for hemoglobin
var <- "percent.hemoglobin"

post_dens_hemo <- ggplot(combmd, aes(x = .data[[var]]+1, y=orig.ident, fill=after_stat(log10(x))))+
  geom_density_ridges_gradient(scale=0.9, stat = "binline", bins = 50) +
  scale_x_log10(labels = scales::label_comma(), name = var,
                limits = c(1,100) )+
  scale_y_discrete(limits = rev(sample_metadata$Code))+
  viridis::scale_fill_viridis( option = "C", alpha = 0.7) +
  theme_ridges()+theme(legend.position = 'none')







### prepare a whole dataset qc summary pdf

# adjust filter summary table colnames, too long
colnames(fs) <- gsub('auto.', '', colnames(fs))

allsampfiltsumm <- paste0(qcdir, '/AllSamples_QC_Summary.pdf')


#add plot titles

dens_UMI <-  dens_UMI + ggtitle('Pre-Filter UMI distribution plot')   
post_dens_UMI <-  post_dens_UMI + ggtitle('Post-Filter UMI distribution plot')   

dens_feature <-  dens_feature + ggtitle('Pre-Filter Unique Gene distribution plot')   
post_dens_feature <- post_dens_feature + ggtitle('Post-Filter Unique Gene distribution plot')   

dens_mito <-  dens_mito + ggtitle('Pre-Filter Percent Mito distribution plot')   
post_dens_mito <-  post_dens_mito + ggtitle('Post-Filter Percent Mito distribution plot')

dens_hemo <-   dens_hemo + ggtitle('Pre-Filter Percent Hemoglobin distribution plot')   
post_dens_hemo <- post_dens_hemo + ggtitle('Post-Filter Percent Hemoglobin distribution plot')   





pdf(allsampfiltsumm, height = 7, width = 7)


print(pdftable(fs, title = "Cell Filter Summary (cell numbers)"))


print( pdftable( round(bsl$nCount_RNA,1) , title = 'Pre-Filter UMI distribution') )

print(  dens_UMI   )
print(  post_dens_UMI   )


print( pdftable( round(bsl$nFeature_RNA,1) , title = 'Pre-Filter Unique Gene distribution') )

print(  dens_feature   )
print(  post_dens_feature  )



print( pdftable( round(bsl$perc.mito,1) , title = 'Pre-Filter Percent Mito distribution') )

print(  dens_mito  )
print(  post_dens_mito   )



print( pdftable( round(bsl$perc.hemoglobin,1) , title = 'Pre-Filter Percent Hemoglobin distribution') )

print(  dens_hemo   )
print(  post_dens_hemo   )

dev.off()

3.1 Filter out poor quality cells

Sometimes poor quality cells can occur in the data due to cell membrane damage during sample preparation, etc. This can bias the downstream analysis.

cat("\n\n

Below we show the number of cells removed with filtering. BasicFilter refers to cells failing a minimum threshold: by default, cells must have >= 500 UMIs, 200 unique genes, and <= 25% mitochondria and hemoglobin content.

Auto.complexity refers to outliers from a regression analysis modelling number of genes by number of UMIs, or the 'complexity' of the cell. Specifically we model the log of each of these, where the relationship is very close to linear. We use a double-regression strategy of both linear and loess regression, and outlier cells must have both a high linear regression Cook's distance and very low Loess negative residuals. This means that cells with a lower than expected number of genes given the number of UMIs are filtered out. Typically, this captures poor quaity cells of extreme low-complexity realy cells such as RBCs.

Auto.libsize refers to cells identified as very low outliers based on median absolute deviation from the general distribtuion of UMIs. Auto.mito refers to cells identified as high outliers based on median absolute deviation from the general distribtuion of mitochondrial content.")

Below we show the number of cells removed with filtering. BasicFilter refers to cells failing a minimum threshold: by default, cells must have >= 500 UMIs, 200 unique genes, and <= 25% mitochondria and hemoglobin content.

Auto.complexity refers to outliers from a regression analysis modelling number of genes by number of UMIs, or the ‘complexity’ of the cell. Specifically we model the log of each of these, where the relationship is very close to linear. We use a double-regression strategy of both linear and loess regression, and outlier cells must have both a high linear regression Cook’s distance and very low Loess negative residuals. This means that cells with a lower than expected number of genes given the number of UMIs are filtered out. Typically, this captures poor quaity cells of extreme low-complexity realy cells such as RBCs.

Auto.libsize refers to cells identified as very low outliers based on median absolute deviation from the general distribtuion of UMIs. Auto.mito refers to cells identified as high outliers based on median absolute deviation from the general distribtuion of mitochondrial content.

knitr::kable(fs)

	BasicFilter	Total	libsize	mito	Unfiltered
Healthy_1	0	800	24	26	750
Healthy_2	1	800	37	20	742
Covid_Mild_1	2	800	25	17	756
Covid_Mild_2	2	707	16	22	667
Covid_Critical_1	1	684	3	31	649
Covid_Critical_2	1	800	7	48	744

cat('\n\n
    
Here we show details of the filtering approach, including the minimum UMI and unique gene cutoffs, maximum percent mito and percent hemoglobin cutoffs. Additionally, we show paramters for the sample-wise cutoffs including median absolute deiation (mad) score threshold, and loess residual threshold. These are used to make tighter cutoffs in an automated, sample-by-sample basis. The actual sample wise cutoffs are provided for each sample in a report in the QC folder called "qualitycontrol_filtering".')

Here we show details of the filtering approach, including the minimum UMI and unique gene cutoffs, maximum percent mito and percent hemoglobin cutoffs. Additionally, we show paramters for the sample-wise cutoffs including median absolute deiation (mad) score threshold, and loess residual threshold. These are used to make tighter cutoffs in an automated, sample-by-sample basis. The actual sample wise cutoffs are provided for each sample in a report in the QC folder called “qualitycontrol_filtering”.

knitr::kable( aflist[[1]]$allcommands )

Command	Option
mad.score.threshold	3
loess_negative_residual_threshold	-5
min_num_UMI	500
min_num_Feature	200
max_perc_mito	25
max_perc_hemoglobin	25
globalfilter.complexity	1
globalfilter.libsize	1
globalfilter.mito	1

3.1.1 UMI distribution and filtering

Unique Molecular Identifiers (UMIs) are sequence labels that are attached to each transcript during library prep, which allow for counting unique mRNA transcripts in 10X data.

cat('Here we show the summary statistics for the distributions of UMIs in each sample before filtering.')

Here we show the summary statistics for the distributions of UMIs in each sample before filtering.

knitr::kable(bsl$nCount_RNA)

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
Healthy_1	512	2315.00	3052.5	3359.045	4054.00	19994
Healthy_2	497	2920.25	3890.5	4200.469	4972.50	17778
Covid_Mild_1	451	2217.25	2874.5	3196.146	3814.75	41783
Covid_Mild_2	486	2156.00	2932.0	3312.269	3847.00	44034
Covid_Critical_1	487	2631.75	3947.5	4475.493	5612.75	39160
Covid_Critical_2	461	2262.50	3287.5	4342.238	4559.50	54956

cat('#### UMI pre-filter distribution
    
Here we plot the distribution of UMIs per cell for all samples before filtering.')

3.1.1.1 UMI pre-filter distribution

Here we plot the distribution of UMIs per cell for all samples before filtering.

print(dens_UMI)

cat('#### UMI post-filter distribution
    
Here we plot the distribution of UMIs per cell for all samples after filtering.')

3.1.1.2 UMI post-filter distribution

Here we plot the distribution of UMIs per cell for all samples after filtering.

print(post_dens_UMI)

3.1.2 Unique feature (gene) distribution and filtering

cat('Here we show the summary statistics for the distributions of genes (or "features", as they are reffered to in Seurat and machine learning jargon) in each sample before filtering.')

Here we show the summary statistics for the distributions of genes (or “features”, as they are reffered to in Seurat and machine learning jargon) in each sample before filtering.

knitr::kable(bsl$nFeature_RNA)

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
Healthy_1	331	915.75	1103.5	1150.872	1319.25	2994
Healthy_2	346	1150.75	1371.5	1407.696	1618.00	3550
Covid_Mild_1	303	826.50	1046.5	1081.787	1259.25	5834
Covid_Mild_2	303	950.00	1202.0	1241.434	1457.00	5096
Covid_Critical_1	302	919.00	1204.5	1288.404	1553.25	4571
Covid_Critical_2	339	882.00	1165.5	1338.001	1549.75	6095

cat('#### Unique Feature pre-filter distribution

Here we plot the distribution of features per cell for all samples before filtering.')

3.1.2.1 Unique Feature pre-filter distribution

Here we plot the distribution of features per cell for all samples before filtering.

print(dens_feature)

cat('#### Unique Feature post-filter distribution
    
Here we plot the distribution of features per cell for all samples after filtering.')

3.1.2.2 Unique Feature post-filter distribution

Here we plot the distribution of features per cell for all samples after filtering.

print(post_dens_feature)

3.1.3 Mitochondrial content level filtering

The percent of mitochondrial content is used as a metric of cell quality in single-cell data. If cells are damaged during handling, they often sustain membrane tearing, which causes cytoplasmic RNA to leak, while mitochondria and mitochondrial RNA is retained. Thus, poor quality, damaged cells often have enriched mt-RNA.

cat('Here we show the summary statistics for the distributions of mitochondrial content in each sample before filtering.')

Here we show the summary statistics for the distributions of mitochondrial content in each sample before filtering.

knitr::kable(bsl$perc.mito)

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
Healthy_1	0.0787608	2.7055380	3.576703	3.754190	4.527047	9.525773
Healthy_2	0.0572082	2.3795023	3.099182	3.325760	3.961888	9.483248
Covid_Mild_1	0.1339457	2.4872762	3.381768	3.670444	4.579035	9.745982
Covid_Mild_2	0.3158310	1.4212632	2.255410	2.610707	3.327504	9.384615
Covid_Critical_1	0.0000000	1.5955738	2.278323	2.567974	3.159081	8.747220
Covid_Critical_2	0.0000000	0.9512908	1.452882	1.883709	2.310267	9.349956

cat('#### Mito pre-filter distribution
    
Here we plot the distribution of mitochondrial content per cell for all samples before filtering.')

3.1.3.1 Mito pre-filter distribution

Here we plot the distribution of mitochondrial content per cell for all samples before filtering.

print(dens_mito)

cat('#### Mito post-filter distribution
    
Here we plot the distribution of mitochondrial content per cell for all samples after filtering.')

3.1.3.2 Mito post-filter distribution

Here we plot the distribution of mitochondrial content per cell for all samples after filtering.

print(post_dens_mito)

3.1.4 Hemoglobin content level filtering

Hemoglobin is expressed strongly but not exclusively by red blood cells (RBCs), which are typically removed during data generation by RBC lysis buffer, because they are extremely numerous and can overwhelm the tissue cells of interest. However, many samples will often still contain some RBCs (or many, if sample quality is poor). Removing them allows focusing in on the cells of interest.

cat('Here we show the summary statistics for the distributions of hemoglobin content in each sample before filtering.')

Here we show the summary statistics for the distributions of hemoglobin content in each sample before filtering.

knitr::kable(bsl$perc.hemoglobin)

	Mean	Max.
Healthy_1	0.0015999	0.2697842
Healthy_2	0.0013343	0.0652316
Covid_Mild_1	0.0013297	0.1336898
Covid_Mild_2	0.0053219	0.3181336
Covid_Critical_1	0.0013056	0.0634921
Covid_Critical_2	0.0007167	0.0833333

cat('#### Hemoglobin pre-filter distribution
    
Here we plot the distribution of hemoglobin content per cell for all samples before filtering.')

3.1.4.1 Hemoglobin pre-filter distribution

Here we plot the distribution of hemoglobin content per cell for all samples before filtering.

print(dens_hemo)

cat('#### Hemoglobin post-filter distribution
    
Here we plot the distribution of hemoglobin content per cell for all samples after filtering.')

3.1.4.2 Hemoglobin post-filter distribution

Here we plot the distribution of hemoglobin content per cell for all samples after filtering.

print(post_dens_hemo)

4 Processing and analysis for each sample

As stated above, below we detail the analysis for each sample, which includes:

Preprocessing: Normalize and scale gene expression with the Seurat SingleCellTransform (SCT) pipeline
Dimension reduction: Principal component analysis, graph construction, clustering, and visualization via UMAP. Ideally, this step requires some hyperparameter selection, including selecting the number of PCs and Louvain clustering resolution
Marker analysis: using a differential expression test, find the unique marker genes of each cluster
Label transfer: from a reference single-cell dataset, quantify how much each cell resembles a known cell type from a reference dataset, such as one derived from a paper or single-cell database

4.1 Select number of Principal Components (PCs) to use based on elbow plot

Principal Component Analysis (PCA) is important for downstream analysis including clustering and visualization with non-linear dimension reduction such as UMAP.

PCA finds high-dimensional planes which vary strongly across the cells. Essentially, each PC consists of very highly correlated genes. The first PC specifically can be thought of as a list of genes that drive together drive the most variance across cells. Each following PC has less variance.

PCA is used to help de-noising the data for downstream tasks like clustering. Individual genes can be noisy, but groups of correlated genes are less noisy.

An important caveat of PCA is that it is a linear method, in that the PC axes it finds are straight lines. This is okay for denoising genes but can often fail to capture the complexity of single-cell RNA-seq and other types of high dimensional genomic datasets. This is why the analysis does not stop at PCA but includes other more complex non-linear methods described below.

One important parameter for downstream analysis is the selection of how many PCs to use. We want to select the PCs that explain a sufficient amount of variance in the data. One way to do that is via “elbow plots” of each PC versus the standard deviation. The cutoff is made at the PC at which the SD stabilizes and becomes horizontal.

Including too few PCs can mean missing important sources of variations downstream and may result in for example, cell types being merged together in a single cluster. Conversely, including too many PCs can introduce noise into the data and result in clusters not easily explained by biology.

By default, we set the number of PCs to use as 30, which will be appropriate for most samples and is recommended as a default by the developers of SingleCellTransform. If the “Elbow” in the elbow plots strongly deviates from this, a different value may be selected and the analysis can be rerun later.

#clean up env
rm(aflist, bsl, combmd, 
   dens_feature, post_dens_feature,
   dens_hemo, post_dens_hemo,
   dens_mito, post_dens_mito,
   dens_UMI, post_dens_UMI,
   fs, maxdens, mdlist,
   submd, featlims, umilims)

invisible(gc(full = T, reset = F, verbose = F))


#test if force_redo is T or if saved sobj result does not exist
## if test == T, do it, if not skip it
### WILL IMPLEMENT LATER


### may implement later: read in serial, process parallel?
# readin serial, split up according to num workers 
# https://stackoverflow.com/questions/3318333/split-a-vector-into-chunks
# x = sample_metadata$code; split(x, ceiling(seq_along(x)/workernum))

# force_redo_test <- T




#read in data and run PCA

sobjlist <- lapply(sample_metadata$Code, function(code){
  
  sobjfile <- paste0(qctmpdir, '/', code, '.rds')
  # sobjfile <- paste0(outdir_indi_seuratobjs, '/SeuratObject-', code, '.rds')
  
  sobj <- readRDS(sobjfile)
  
  DefaultAssay(sobj) <- 'RNA'
  
  sobj <- SCTransform(sobj, assay = "RNA", verbose = verbose, method = 'glmGamPoi', vst.flavor='v2')
  
  sobj <- RunPCA(sobj, assay = "SCT", verbose = verbose)
  
  sobj
  
})

names(sobjlist) <- sample_metadata$Code

# #read from tmp dir
# sobjfile <- paste0(qctmpdir, '/SeuratObject-', code, '.rds')

# #remove tmp dir
unlink(qctmpdir, recursive = T)



## check elbow plot

elbowplots <- lapply(sobjlist, function(sobj){
  
  ElbowPlot(sobj, ndims = 50) + ggtitle(sobj@project.name)
  
})

#plot one at a time rather than side by side, label was getting cut off
#patchwork::wrap_plots(elbowplots)

for(i in 1:length(elbowplots)){
  print(elbowplots[[i]] + geom_vline(xintercept = pcs_indi, linetype = 'dotted', color = 'red'))
}

4.2 Clustering

Once PCA is completed and the number of critical PCs is selected, we perform a number of steps that all involve grouping cells together based on shared transcriptomic patterns:

k-nearest neighbor graph construction
Louvain clustering
Non-linear dimensionality reduction for visualization, such as t-SNE or UMAP

This process allows us to group cells together based on transcriptomic similarity. Louvain clustering optionally allows for input of a hyperparameter called “resolution,” with high resolution finding larger numbers of clusters. By default, we set this value to 0.5.

#clean env
rm(elbowplots)
invisible(gc(full = T, reset = F, verbose = F))


# ## using ndims_vec, continue individual sample pre-processing.
# 
# Calculate graph, clustering, and umap.

sobjlist <- lapply(sobjlist, function(sobj){
  
  # ndim_sample <- ndims_vec[sobj@project.name] #sample-wise PC selection
  ndim_sample <- pcs_indi
  
  sobj <- FindNeighbors(sobj, reduction = "pca", dims = c(1:ndim_sample), verbose = verbose)
  sobj <- FindClusters(sobj, verbose = verbose, resolution = res_indi)
  
  # #add 3d UMAP first
  # sobj <- Seurat::RunUMAP(sobj, reduction = "pca", dims = c(1:ndim_sample),
  #                         n.components = 3, 
  #                         reduction.name = 'UMAP3D', reduction.key = 'UMAP3D_')
  #add normal UMAP
  sobj <- RunUMAP(sobj, reduction = "pca", dims = c(1:ndim_sample), verbose = verbose)
  
  sobj
  
  
})

# ```




# ```{r markers_individualsamps_clusters}


# ## calulcate markers for individual sample clusters
# 
# This is parallelized with Future as per Seurat recommendations.
# Future multisession mode.

#turn off future, it seems to break things

# future::plan('multisession', workers=workernum)




# save markers
indimarkerdir <- paste0(outdir_indi, '/individualsample_clustermarkers/')

sampmarkersave <- paste0(indimarkerdir, '/markers-PCs_', pcs_indi, '-res_', res_indi, '/' )
dir.create(sampmarkersave, recursive = T)




mlist_individualsamples_clusters <- lapply(sample_metadata$Code, function(code){
  
  
  sampmarkersave_eachsamp <- paste0(sampmarkersave, code, '_clustermarkers.csv')
  
  #turn off file exists read in, too dangerous
  # if(!file.exists(sampmarkersave_eachsamp)){
  #   
  sobj <- sobjlist[[code]]
  
  m <- FindAllMarkers(sobj, only.pos = T, verbose = verbose)
  # as of Nov 9 2023 (Seurat v5): add score to markers
  m$score <- (m$pct.1 - m$pct.2) * m$avg_log2FC
  
  
  write.csv(m, sampmarkersave_eachsamp, quote = F, row.names = F)
  #   
  # } else{
  # m <- read.csv(sampmarkersave_eachsamp)
  
  
  # }
  
  return(m)
  
  
  
})

names(mlist_individualsamples_clusters) <- sample_metadata$Code



# future::plan(strategy = 'sequential')

if(use_labeltransfer == T){
  
  
  text <- '## Label transfer from reference scRNA-seq data

With a reference single-cell RNA-seq dataset, we use label transfer to infer which celltypes are present in the new data.

One way to do this is via "label transfer", an integration-based machine learning method for classification. We use the [Seurat method](https://www.cell.com/cell/fulltext/S0092-8674(19)30559-8) for label transfer here.

'
  
  cat(text)
  
}

4.3 Label transfer from reference scRNA-seq data

With a reference single-cell RNA-seq dataset, we use label transfer to infer which celltypes are present in the new data.

One way to do this is via “label transfer”, an integration-based machine learning method for classification. We use the Seurat method for label transfer here.

if(use_labeltransfer == T){
  
  
  labeltransfer_outdir_indi <- paste0(outdir_indi, '/labeltransfer/')
  dir.create(labeltransfer_outdir_indi, recursive = T)
  
  reference <- readRDS(refdatapath)
  
  
  ### unfortunately, Seurat does not like underscores in feature names, so we need to replace
  if( any(grepl('_', reference$Celltype)) ){
    ct <- reference$Celltype
    ct <- as.character(ct)
    ct <- gsub('_', '-', ct)
    ct <- factor(ct, levels = names(sort(table(ct), decreasing = T)) )
    reference$Celltype <- ct
  }
  
  
  # #pre-process the reference data
  # # make sure this is done beforehand!!!
  # reference <- SCTransform(reference, ncells = 3000, verbose = verbose) %>%
  #   RunPCA(verbose = verbose) %>%
  #   RunUMAP(dims = 1:30, verbose = verbose)
  # 
  # reference$Celltype <- reference$subclass
  # 
  # refdatapath = "data/vignette/allen_cortex_preproc.rds"
  # saveRDS(reference, refdatapath)
  
  ### actually label-transfer ###
  
  
  
  sobjlist <- lapply(sobjlist, function(sobj){
    
    if(verbose == T){ message('\nLabel transfer for: ',sobj@project.name, '\n') }
    
    
    #get the integration score matrix
    
    
    # if( SeuratLabelTransfer.normalization.method == 'auto' ){
    #   
    #   
    #   if('SCT' %in% names(reference@assays) ){
    #     SeuratLabelTransfer.normalization.method <- 'SCT'
    #   } else{
    #     SeuratLabelTransfer.normalization.method <- 'LogNormalize'
    #   }
    #   
    #   
    # }
    SeuratLabelTransfer.normalization.method = 'SCT'
    
    anchors <- FindTransferAnchors(reference = reference, query = sobj, normalization.method = SeuratLabelTransfer.normalization.method, verbose = verbose)
    
    predictions.assay <- TransferData(anchorset = anchors, 
                                      refdata = reference$Celltype, 
                                      prediction.assay = T,
                                      weight.reduction = sobj[["pca"]], dims = 1:30, verbose = verbose)
    
    
    sobj[["predictions"]] <- predictions.assay
    
    
    #get the top calls for each cell
    
    topcalls <- TransferData(anchorset = anchors, refdata = reference$Celltype, prediction.assay = F,
                             weight.reduction = sobj[["pca"]], dims = 1:30, verbose = verbose)
    
    levs <- names( sort(table(topcalls$predicted.id), decreasing = T) )
    sobj$top_celltype_call_seurat <- factor(topcalls$predicted.id, levels = levs)
    sobj$top_celltype_call_seurat_score <- topcalls$prediction.score.max
    
    #add thresholded score
    md <- sobj@meta.data
    md$top_celltype_thresholded <- md$top_celltype_call_seurat
    md[md$top_celltype_call_seurat_score < 0.3, 'top_celltype_thresholded'] <- NA
    sobj$top_celltype_thresholded <- md$top_celltype_thresholded
    
    
    
    
    ### get cluster-wise labeltransfer max ###
    #get predmat
    predmat <- sobj@assays$predictions@data
    
    #drop "max"
    predmat <- predmat[1:(nrow(predmat)-1), ]
    
    #for each cluster, get max
    md <- sobj@meta.data
    clustname <- 'seurat_clusters'
    
    clustmaxlist <- lapply(levels(md[,clustname]), function(clust){
      clustcells <- rownames( md[md[,clustname] == clust,] )
      clustpred <- predmat[,colnames(predmat) %in% clustcells,drop=F]
      
      clust_avgscores <- Matrix::rowMeans(clustpred)
      maxscore <- clust_avgscores[which.max(clust_avgscores)]
      
      data.frame(cluster = clust, max = names(maxscore), score = maxscore, row.names = NULL)
      
    })
    
    clustmaxdf <- dplyr::bind_rows(clustmaxlist)
    
    
    #make a label
    clustmaxdf$label <- paste0( clustmaxdf$max)
    
    
    #uncertainty... if < 0.3, put as NA
    # if between 0.3 - 0.6, put putative
    clustmaxdf[clustmaxdf$score < 0.3,'label'] <- NA
    clustmaxdf[clustmaxdf$score >= 0.3 & clustmaxdf$score < 0.5,'label'] <- paste0( clustmaxdf[clustmaxdf$score >= 0.3 & clustmaxdf$score < 0.5,'label'],
                                                                                    '_putative')
    
    
    
    
    
    #add to metadata
    sobj$celltype_cluster_prediction <- sobj$seurat_clusters
    sobj$celltype_cluster_prediction <- plyr::mapvalues(sobj$celltype_cluster_prediction,
                                                        from = levels(sobj$celltype_cluster_prediction),
                                                        to = clustmaxdf$label)
    
    
    #write out cluster-celltype mapping
    labeltransfer_outdir_indi_code <- paste0(labeltransfer_outdir_indi, '/', sobj@project.name, '/')
    dir.create(labeltransfer_outdir_indi_code)
    
    clustcelltypemapfile <- paste0(labeltransfer_outdir_indi_code, '/ClusterCelltypeMapping.csv')
    write.csv(x=clustmaxdf,
              file = clustcelltypemapfile, 
              row.names = F, quote = F)
    
    
    sobj
    
  })
  
  
  
  
  
  
  
  
  
  # m_reference <- readRDS(m_reference)
  
  # actually we only need this later. read in at the righ time. for now save path
  m_reference_path <- m_reference
  
  
  
  
  
  
  rm(reference) #for memory saving
  
  
  # ```
  
  invisible(gc(full = T, reset = F, verbose = F))
  
  
  
}

5 Individual sample clustering summary plots (pre-integration)

In each sample, we perform clustering and label transfer. Here we plot the clusters and cluster markers for each sample. This can help identify which cell types are present in each sample.

#save each as pdf, one per sample...
dir.create( paste0(outdir_indi, '/individualsample_plots/') , recursive = T)


#prep summary plots for each sample
# umap of clusters
# insert QC per cluster plots here?
# cluster markers

summaryplots_individualsamples <- lapply( sobjlist , function(sobj){
  
  # set up title
  sampname <- sobj@project.name
  
  #get markers
  m <- mlist_individualsamples_clusters[[sampname]]
  
  
  
  #clusters plot
  # for auto plotting with manually set res, need to use paste here...
  
  plottingvar <- paste0('SCT_snn_res.', res_indi)
  
  #dimplot of clusters
  d1_a <- wrap_plots(
    DimPlot(sobj, group.by = plottingvar, label = T, repel = T)
  ) + plot_annotation(title = sampname, caption = 'Louvain Clusters plotted on UMAP')
  
  
  
  # ## 3d umap ##
  # embs <- as.data.frame(sobj@reductions$UMAP3D@cell.embeddings)
  # embs <- cbind(embs, sobj$seurat_clusters); colnames(embs)[4] <- 'seurat_clusters'
  # embs$cluster_color <- plyr::mapvalues(embs$seurat_clusters, 
  #                                       from = levels(embs$seurat_clusters), 
  #                                       to = scales::hue_pal()(length(levels(embs$seurat_clusters))))
  # #add labels...
  # labdf <- aggregate(cbind(UMAP3D_1, UMAP3D_2, UMAP3D_3) ~ seurat_clusters, embs, median)
  # labdf$cluster_color <- levels(embs$cluster_color)
  # 
  # #store inrgedients, run rgl when its time to print
  # three_d_umap_ingrdients <- list(embs = embs, labdf=labdf)
  # 
  # 
  
  
  # qc plots, do it with patchwork
  d0 <- wrap_plots(ncol = 2, list(
    VlnPlot(sobj, 'nCount_RNA', pt.size = 0.1)+NoLegend() ,
    VlnPlot(sobj, 'nFeature_RNA', pt.size = 0.1)+NoLegend(),
    VlnPlot(sobj, 'percent.mito', pt.size = 0.1)+NoLegend() ,
    VlnPlot(sobj, 'percent.hemoglobin', pt.size = 0.1)+NoLegend()
  )) + plot_annotation(title = sampname)
  
  
  # cluster markers
  
  
  DefaultAssay(sobj) <- 'SCT'
  n <- 5
  top <- m %>% group_by(cluster) %>% top_n(n = n, wt = score)
  genes <- top$gene
  #make sure genes are in 
  if( any( !(genes %in% rownames(sobj@assays$SCT@scale.data)) ) ){
    
    #try getresidual...
    missinggenes <- genes[!(genes %in% rownames(sobj@assays$SCT@scale.data))]
    sobj <- GetResidual(sobj, missinggenes, na.rm = F, replace.value = T)
    
    #it can be complicated doing this after integration, some genes are NAs... 
    scgem <- sobj@assays$SCT@scale.data
    
    if( any( !complete.cases(scgem) ) ){
      scgem <- scgem[complete.cases(scgem),]
      top <- top[top$gene %in% rownames(scgem),]
      sobj@assays$SCT@scale.data <- scgem
    }
    rm(scgem)
    
  }
  
  #prep heatmap
  top <- top[top$gene %in% rownames(sobj),]
  gem <- sobj@assays$SCT@scale.data
  gem <- gem[match(top$gene, rownames(gem)),]
  #annot for clusters
  #first order gem by cluster...
  md <- sobj@meta.data
  md <- md[order(md$seurat_clusters),]
  gem <- gem[,match(rownames(md), colnames(gem))]
  clust_bc <- setNames(md$seurat_clusters,
                       nm = colnames(gem)
  )
  col_clust <- setNames(scales::hue_pal()(length(levels(sobj$seurat_clusters))),
                        nm = levels(sobj$seurat_clusters))
  ha_clust <- ComplexHeatmap::HeatmapAnnotation(Cluster = clust_bc, col = list(Cluster = col_clust), show_legend = F)
  #annot for markers
  #set genes according to ct_ordered
  top$cluster <-factor(top$cluster, levels=unique(top$cluster))
  # top <- top[order(top$cluster),]
  gem <- gem[match(top$gene, rownames(gem)),]
  ct_gene <- setNames(top$cluster,
                      nm=top$gene)
  col_gene <- col_clust
  col_gene <- col_gene[names(col_gene) %in% top$cluster]
  ha_genes <- ComplexHeatmap::rowAnnotation(Cluster = ct_gene, col = list(Cluster = col_gene),
                                            show_annotation_name=F)
  #restrict range
  gem[gem>5] <- 5
  gem[gem<-5] <- -5
  
  #actual heatmap
  hm <- ComplexHeatmap::Heatmap(gem,
                                # column_title = 'Integrated clusters',
                                column_labels = rep('', ncol(gem)),
                                row_names_gp = grid::gpar(fontsize = 5),
                                column_split = md$seurat_clusters,
                                row_split = top$cluster,
                                row_title_gp = grid::gpar(fontsize = 5),
                                row_gap = unit(0.8, "mm"), 
                                column_gap = unit(0.8, "mm"),
                                row_title_rot = 0,
                                column_title_rot = 45,
                                column_title_gp = grid::gpar(fontsize = 7),
                                name = 'Scaled\nExpression',
                                cluster_columns = F,
                                cluster_rows = F,
                                top_annotation = ha_clust,
                                left_annotation = ha_genes,
                                use_raster = F)
  
  # invisible(hm <- draw(hm, column_title = sampname))
  
  
  list(
    d1_a=d1_a,
    # three_d_umap_ingrdients = three_d_umap_ingrdients,
    
    hm=hm,
    d0=d0
  )
  
} )


#name the list elements, one per sample

names(summaryplots_individualsamples) <- sapply(sobjlist, function(sobj){sobj@project.name}, simplify = T)





#save as PDFs
lapply(names(summaryplots_individualsamples), function(sampname){
  pdf( paste0(outdir_indi, '/individualsample_plots/', sampname, '.pdf'), width = 9, height = 9 )
  
  print( summaryplots_individualsamples[[sampname]]$d1_a )
  print( summaryplots_individualsamples[[sampname]]$hm )
  print( summaryplots_individualsamples[[sampname]]$d0 )
  
  dev.off()
  
  
})




if(use_labeltransfer == T){
  
  
  
  ## plots of cell type predictions from label transfer
  
  ctplots_individualsamples <- lapply( sobjlist, function(sobj){
    
    #set up title
    sampname <- sobj@project.name
    
    
    
    
    #clusters plot
    # for auto plotting with manually set res, need to use paste here...
    
    plottingvar <- paste0('SCT_snn_res.', res_indi)
    
    #dimplot of clusters
    d1_a <- wrap_plots(
      DimPlot(sobj, group.by = plottingvar, label = T, repel = T)
    ) + plot_annotation(title = sampname, caption = 'Louvain Clusters plotted on UMAP')
    
    
    
    
    ### prep the Celltype Plots ("ctplots")
    
    
    # violinplots of all celltypes
    
    #get exp of the celltypes
    rs <- Matrix::rowSums(sobj@assays$predictions@data)
    rs <- head( rs , -1)
    
    #keep only celltypes that are exp
    rs <- sort(rs[rs>0], decreasing = T)
    ct_in <- names(rs)
    
    #plots of all celltypes in data...
    DefaultAssay(sobj) <- 'predictions'
    
    ct_plots <- lapply(setNames(ct_in,ct_in), function(ct){
      #FeaturePlot(sobj, features = ct) + ggtitle(sampname)
      
      ct_plot <-  VlnPlot(sobj, ct)+NoLegend()+ylab('Prediction score') + 
        plot_annotation(title = sampname)
      
      return(ct_plot)
      
    })
    DefaultAssay(sobj) <- 'SCT'
    
    
    
    
    #heatmap of the prediction scores per cluster
    # use scaled values, comparable between clusters
    # get avgs
    avgs <- AverageExpression(sobj, assays = 'predictions', return.seurat = F)
    #remove max
    avgs <- head( as.data.frame(avgs) , -1)
    # remove empty prediction rows with all 0s
    avgs <- avgs[Matrix::rowSums(avgs)>0,]
    #make column titles nicer
    colnames(avgs) <- gsub('predictions.', 'cluster_', colnames(avgs))
    #format as numeric matrix
    avgs <- as.matrix(avgs)
    ### scale --> this emphasizes diffs between clusters, seems to put related cell types together
    avgs <- t(scale(t(avgs)))
    #select middle value  for color scale
    medval <- mean(avgs)
    #plot it
    hm_ctscores <- ComplexHeatmap::Heatmap(avgs,
                                           name = 'Scaled\nmean prediction\nscores per cluster',
                                           column_title = sampname,
                                           rect_gp = grid::gpar(col = "white", lwd = 0.5),
                                           circlize::colorRamp2(c(min(avgs), medval, max(avgs)), c("blue", "white", "red")))
    
    
    
    
    
    #for dotplot, add dendrogram and row label order
    # it may throw a warning about drawing plot first etc, 
    #shouldn't be an issue if we set seed, which we did
    suppressWarnings(
      dend <- row_dend(hm_ctscores)
    )
    
    #get ordered row labels for dotplot and other plots
    suppressWarnings(
      ct_ordered <- rownames(hm_ctscores@matrix)[ComplexHeatmap::row_order(hm_ctscores)]
    )
    
    
    nicedend <- ggdendro::ggdendrogram(rev(dend), rotate = T) +   
      scale_y_reverse(expand = c(0.05, 0))+
      theme(axis.text.y = element_blank(),
            axis.text.x = element_blank())
    
    dp_ctscores <- DotPlot(sobj, assay = 'predictions', rev(ct_ordered)) + 
      coord_flip() + 
      theme(axis.title.y=element_blank(),
            axis.text.y =  element_text(hjust = 0),
            axis.text.x = element_text(size=5) )+
      scale_color_gradient2(low = 'blue', high = 'red', mid = 'grey')+
      xlab(label = 'Cluster')+
      guides(color = guide_colorbar(title = "Scaled Average\nPrediction Score"))
    
    dp_ctscores <- patchwork::wrap_plots(list(nicedend, dp_ctscores), widths = c(0.2,1))
    
    
    
    
    
    # heatmap of reference markers
    m_reference <- readRDS(m_reference_path)
    
    
    #make sure to remove underscores, since seurat doesn't like it in label transfer feature names
    m_ref_small <- m_reference
    
    lt_ref_levs <- levels(m_ref_small$cluster)
    lt_ref_levs <- gsub('_', '-', lt_ref_levs)
    m_ref_small$cluster <- plyr::mapvalues(m_ref_small$cluster, from = levels(m_ref_small$cluster), lt_ref_levs)
    
    m_ref_small <- m_ref_small[m_ref_small$gene %in% rownames(sobj),]
    m_ref_small <- m_ref_small[m_ref_small$cluster %in% ct_in,]
    n <- 5
    top <- m_ref_small %>% group_by(cluster) %>% top_n(n = n, wt = avg_log2FC)
    
    genes <- top$gene
    
    
    #make sure genes are in 
    if( any( !(genes %in% rownames(sobj@assays$SCT@scale.data)) ) ){
      missinggenes <- genes[!(genes %in% rownames(sobj@assays$SCT@scale.data))]
      sobj <- GetResidual(sobj, genes)
    }
    
    #prep heatmap
    top <- top[top$gene %in% rownames(sobj),]
    gem <- sobj@assays$SCT@scale.data
    gem <- gem[match(top$gene, rownames(gem)),]
    
    #annot for clusters
    #first order gem by cluster...
    md <- sobj@meta.data
    md <- md[order(md$seurat_clusters),]
    gem <- gem[,match(rownames(md), colnames(gem))]
    
    clust_bc <- setNames(md$seurat_clusters,
                         nm = colnames(gem)
    )
    
    col_clust <- setNames(scales::hue_pal()(length(levels(sobj$seurat_clusters))),
                          nm = levels(sobj$seurat_clusters))
    
    ha_clust <- ComplexHeatmap::HeatmapAnnotation(Cluster = clust_bc, 
                                                  col = list(Cluster = col_clust),
                                                  show_legend = F
    )
    
    
    
    #annot for markers
    
    #set genes according to ct_ordered
    top$cluster <-factor(top$cluster, levels=ct_ordered)
    top <- top[order(top$cluster),]
    gem <- gem[match(top$gene, rownames(gem)),]
    
    ct_gene <- setNames(top$cluster,
                        nm=top$gene)
    coul <- RColorBrewer::brewer.pal(8, "Set2") 
    coul <- colorRampPalette(coul)(length(unique(top$cluster)))
    col_gene <- setNames(coul, nm = unique(top$cluster))
    
    ha_genes <- ComplexHeatmap::rowAnnotation(Celltype = ct_gene, col = list(Celltype = col_gene),
                                              show_annotation_name=F)
    
    
    #restrict range
    gem[gem>5] <- 5
    gem[gem<-5] <- -5
    
    
    #actual heatmap
    hm_refmarkers <- ComplexHeatmap::Heatmap(gem,
                                             #column_title = sampname,
                                             column_labels = rep('', ncol(gem)),
                                             row_names_gp = grid::gpar(fontsize = 5),
                                             column_split = md[,paste0('SCT_snn_res.', res_indi)],
                                             row_split = top$cluster,
                                             row_title_gp = grid::gpar(fontsize = 5),
                                             row_gap = unit(0.8, "mm"), 
                                             column_gap = unit(0.8, "mm"),
                                             column_title_rot = 45,
                                             column_title_gp = grid::gpar(fontsize = 7),
                                             row_title_rot = 0,
                                             name = 'Scaled\nExpression',
                                             cluster_columns = F,
                                             cluster_rows = F,
                                             top_annotation = ha_clust,
                                             left_annotation = ha_genes,
                                             use_raster = F)
    
    
    # hm_refmarkers <- draw(hm_refmarkers, column_title = sampname)
    
    
    
    #try to average the matrix
    avgl <- lapply(levels(md$seurat_clusters), function(clust){
      mdc <- md[md$seurat_clusters==clust,]
      gemc <- gem[,colnames(gem) %in% rownames(mdc)]
      avg <- matrix(rowMeans(gemc), 
                    dimnames = list(rownames(gem), clust))
      avg
    })
    avg <- do.call('cbind',avgl)
    
    #need to re-prep column annot
    clust_bc <- factor(str_sort(colnames(avg), numeric = T), levels = str_sort(colnames(avg), numeric = T))
    
    col_clust <- setNames(scales::hue_pal()(length(levels(sobj$seurat_clusters))),
                          nm = levels(sobj$seurat_clusters))
    
    ha_clust <- ComplexHeatmap::HeatmapAnnotation(Cluster = clust_bc, col = list(Cluster = col_clust), show_legend = F)
    
    
    hm_refmarkers_avg <- ComplexHeatmap::Heatmap(avg,
                                                 #column_title = sampname,
                                                 row_names_gp = grid::gpar(fontsize = 6),
                                                 column_split = factor(str_sort(colnames(avg), numeric = T), levels = str_sort(colnames(avg), numeric = T)), 
                                                 column_names_gp = grid::gpar(fontsize = 6),
                                                 column_title_rot = 45,
                                                 column_names_rot = 0, 
                                                 
                                                 row_split = top$cluster,
                                                 row_title_gp = grid::gpar(fontsize = 6),
                                                 row_gap = unit(0.5, "mm"), 
                                                 column_gap = unit(0.5, "mm"),
                                                 row_title_rot = 0,
                                                 name = 'Scaled\nExpression',
                                                 cluster_columns = F,
                                                 cluster_rows = F,
                                                 top_annotation = ha_clust,
                                                 left_annotation = ha_genes,
                                                 use_raster = F)
    
    
    # hm_refmarkers_avg <- draw(hm_refmarkers_avg, column_title = sampname)
    
    
    
    
    #top cell type plots
    plottingvar <- 'top_celltype_thresholded'
    
    
    
    d2_a <- wrap_plots(
      DimPlot(sobj, group.by = plottingvar, label = T, repel = T)
    ) + plot_annotation(title = sampname)
    
    d2_b <- wrap_plots(
      FeaturePlot(sobj, 'top_celltype_call_seurat_score')) + plot_annotation(title = sampname)
    
    #alluvial plot, clusters to cell types
    md <- sobj@meta.data
    labelsdf <- md[,c('seurat_clusters', 'top_celltype_thresholded')]
    
    
    ap <- wrap_plots( alluvialplot(labelsdf, repel = T, direction='y')
    ) + plot_annotation(title = sampname)
    
    
    
    ## read in cluster CT mapping
    #write out cluster-celltype mapping
    labeltransfer_outdir_indi_code <- paste0(labeltransfer_outdir_indi, '/', sobj@project.name, '/')
    
    clustcelltypemapfile <- paste0(labeltransfer_outdir_indi_code, '/ClusterCelltypeMapping.csv')
    
    clustmaxdf <- read.csv(clustcelltypemapfile)
    
    
    #also plot the cluster celltype call on dimplot
    d3 <- DimPlot(sobj, group.by = 'celltype_cluster_prediction', label = T, repel = T)
    
    
    
    #using heatmap dendrogram, order in a cool way
    suppressWarnings(
      ct_ordered <- rownames(hm_ctscores@matrix)[ComplexHeatmap::row_order(hm_ctscores)]
    )
    
    ct_plots <- ct_plots[ct_ordered] 
    
    
    
    list(d1_a=d1_a, 
         hm_ctscores=hm_ctscores,
         dp_ctscores=dp_ctscores,
         hm_refmarkers=hm_refmarkers,
         hm_refmarkers_avg=hm_refmarkers_avg,
         
         d2_a=d2_a,
         d2_b=d2_b,
         ap=ap,
         
         d3 = d3,
         clustmaxdf=clustmaxdf,
         
         ct_plots=ct_plots
    )
    
    
  })
  
  
  #name them with the code names
  
  names(ctplots_individualsamples) <- sapply(sobjlist, function(sobj){sobj@project.name}, simplify = T)
  
  #save as PDFs
  
  lapply(names(ctplots_individualsamples), function(sampname){
    
    #print to labeltransfer df
    
    labeltransfer_outdir_indi_code <- paste0(labeltransfer_outdir_indi, '/', sampname, '/')
    pdf( paste0(labeltransfer_outdir_indi_code, '/LabelTransferSummaryPlots.pdf'), width = 9, height = 9 )
    
    ## print each but make sure table gets printed as a pdftable ##
    ctplots_thissamp <- ctplots_individualsamples[[sampname]]
    for(i in 1:length(ctplots_thissamp) ){
      
      if(names(ctplots_thissamp)[i] == 'clustmaxdf'){
        print( scDAPP::pdftable(ctplots_thissamp[[i]], title = 'Cluster-Celltype Mapping') )
      } else{
        print(ctplots_thissamp[[i]])
      }
      
    }
    
    dev.off()
    
    return(sampname)
    
  })
  
  
  
}

#print to report

# do this in a way that creates a section for each sample...
# https://stackoverflow.com/questions/36674824/use-loop-to-generate-section-of-text-in-rmarkdown

template <- "


## Sample %s


" # don't forget the newline




for (i in 1:length( names(summaryplots_individualsamples) ) ) {
  sampname <- names(summaryplots_individualsamples)[i]
  
  cat(sprintf(template, sampname))
  
  
  sampsumplots <- summaryplots_individualsamples[[i]]
  
  
  
  #plot each plot one at a time with appropriate label
  
  plotlab <- "


### UMAP of clusters

Here we plot a UMAP of the Louvain clusters for this sample.


"
  
  cat( plotlab )
  print( sampsumplots$d1_a )
  
  
  
  #   ## 3d umap ##
  #   
  #   
  #   plotlab <- "
  # 
  # 
  # ### 3D UMAP of clusters
  # 
  # Here we plot a 3D UMAP of the Louvain clusters for this sample.
  # 
  # 
  # "
  #   
  #   cat( plotlab )
  #   print( sampsumplots$d1_a )
  #   
  #   embs <- as.data.frame(sobj@reductions$UMAP3D@cell.embeddings)
  #   embs <- cbind(embs, sobj$seurat_clusters); colnames(embs)[4] <- 'seurat_clusters'
  #   embs$cluster_color <- plyr::mapvalues(embs$seurat_clusters, 
  #                                         from = levels(embs$seurat_clusters), 
  #                                         to = scales::hue_pal()(length(levels(embs$seurat_clusters))))
  #   #add labels...
  #   labdf <- aggregate(cbind(UMAP3D_1, UMAP3D_2, UMAP3D_3) ~ seurat_clusters, embs, median)
  #   labdf$cluster_color <- levels(embs$cluster_color)
  #   
  #   #store inrgedients, run rgl when its time to print
  #   three_d_umap_ingrdients <- summaryplots_individualsamples$three_d_umap_ingrdients
  #   embs <- three_d_umap_ingrdients$embs
  #   labdf <- three_d_umap_ingrdients$labdf
  #   
  #   
  #   UMAP3D_1 = embs$UMAP3D_1; UMAP3D_2 = embs$UMAP3D_2; UMAP3D_3 = embs$UMAP3D_3
  #   rgl::plot3d(UMAP3D_1, UMAP3D_2, UMAP3D_3, col = embs$cluster_color)
  #   rgl::text3d(labdf$UMAP3D_1, labdf$UMAP3D_2, labdf$UMAP3D_3, texts = labdf$seurat_clusters, 
  #               adj = c(2,2,2))
  #   
  #   
  
  
  plotlab <- "


### Heatmap of cluster markers

Here we plot the top 5 marker genes of each cluster as identified by wilcoxon test. Sometimes, clusters may share the same markers, which may indicate the clusters are similar, such as subtypes of the same cell type (for example, CD4 vs CD8 T cells).


"
  
  cat( plotlab )
  print( sampsumplots$hm )
  
  
  plotlab <- "


### QC plots 

Here we plot some QC information, including the number of UMIs (nCount_RNA) and the number of unique genes (nFeature_RNA). Very low values may indicate a low number of cells in a specific area or low quality. If the clustering and markers look very strange, the quality metrics may help diagnose issues by indicating a cluster of poor quality cells.


"
  
  cat( plotlab )
  print( sampsumplots$d0 )
  
}

5.1 Sample Healthy_1

5.1.1 UMAP of clusters

Here we plot a UMAP of the Louvain clusters for this sample.

5.1.2 Heatmap of cluster markers

Here we plot the top 5 marker genes of each cluster as identified by wilcoxon test. Sometimes, clusters may share the same markers, which may indicate the clusters are similar, such as subtypes of the same cell type (for example, CD4 vs CD8 T cells).

5.1.3 QC plots

Here we plot some QC information, including the number of UMIs (nCount_RNA) and the number of unique genes (nFeature_RNA). Very low values may indicate a low number of cells in a specific area or low quality. If the clustering and markers look very strange, the quality metrics may help diagnose issues by indicating a cluster of poor quality cells.

5.2 Sample Healthy_2

5.2.1 UMAP of clusters

Here we plot a UMAP of the Louvain clusters for this sample.

5.2.2 Heatmap of cluster markers

Here we plot the top 5 marker genes of each cluster as identified by wilcoxon test. Sometimes, clusters may share the same markers, which may indicate the clusters are similar, such as subtypes of the same cell type (for example, CD4 vs CD8 T cells).

5.2.3 QC plots

Here we plot some QC information, including the number of UMIs (nCount_RNA) and the number of unique genes (nFeature_RNA). Very low values may indicate a low number of cells in a specific area or low quality. If the clustering and markers look very strange, the quality metrics may help diagnose issues by indicating a cluster of poor quality cells.

5.3 Sample Covid_Mild_1

5.3.1 UMAP of clusters

Here we plot a UMAP of the Louvain clusters for this sample.

5.3.2 Heatmap of cluster markers

Here we plot the top 5 marker genes of each cluster as identified by wilcoxon test. Sometimes, clusters may share the same markers, which may indicate the clusters are similar, such as subtypes of the same cell type (for example, CD4 vs CD8 T cells).

5.3.3 QC plots

Here we plot some QC information, including the number of UMIs (nCount_RNA) and the number of unique genes (nFeature_RNA). Very low values may indicate a low number of cells in a specific area or low quality. If the clustering and markers look very strange, the quality metrics may help diagnose issues by indicating a cluster of poor quality cells.

5.4 Sample Covid_Mild_2

5.4.1 UMAP of clusters

Here we plot a UMAP of the Louvain clusters for this sample.

5.4.2 Heatmap of cluster markers

Here we plot the top 5 marker genes of each cluster as identified by wilcoxon test. Sometimes, clusters may share the same markers, which may indicate the clusters are similar, such as subtypes of the same cell type (for example, CD4 vs CD8 T cells).

5.4.3 QC plots

Here we plot some QC information, including the number of UMIs (nCount_RNA) and the number of unique genes (nFeature_RNA). Very low values may indicate a low number of cells in a specific area or low quality. If the clustering and markers look very strange, the quality metrics may help diagnose issues by indicating a cluster of poor quality cells.

5.5 Sample Covid_Critical_1

5.5.1 UMAP of clusters

Here we plot a UMAP of the Louvain clusters for this sample.

5.5.2 Heatmap of cluster markers

Here we plot the top 5 marker genes of each cluster as identified by wilcoxon test. Sometimes, clusters may share the same markers, which may indicate the clusters are similar, such as subtypes of the same cell type (for example, CD4 vs CD8 T cells).

5.5.3 QC plots

Here we plot some QC information, including the number of UMIs (nCount_RNA) and the number of unique genes (nFeature_RNA). Very low values may indicate a low number of cells in a specific area or low quality. If the clustering and markers look very strange, the quality metrics may help diagnose issues by indicating a cluster of poor quality cells.

5.6 Sample Covid_Critical_2

5.6.1 UMAP of clusters

Here we plot a UMAP of the Louvain clusters for this sample.

5.6.2 Heatmap of cluster markers

Here we plot the top 5 marker genes of each cluster as identified by wilcoxon test. Sometimes, clusters may share the same markers, which may indicate the clusters are similar, such as subtypes of the same cell type (for example, CD4 vs CD8 T cells).

5.6.3 QC plots

Here we plot some QC information, including the number of UMIs (nCount_RNA) and the number of unique genes (nFeature_RNA). Very low values may indicate a low number of cells in a specific area or low quality. If the clustering and markers look very strange, the quality metrics may help diagnose issues by indicating a cluster of poor quality cells.

if(use_labeltransfer == T){
  
  
  text <- '
# Individual sample label transfer cell type predictions

For each sample we perform label transfer from a reference single-cell RNA-seq dataset. Here, we plot the results of the label transfer. Using a prior single-cell RNAseq dataset, we learn which cell types are present in the current data.

Using label transfer, we can get a score for each cell type in the reference dataset. A high score indicates a strong and confident match, while a low score indicates a match of low confidence and may represent a cell type that was not captured in the reference.

Note that we apply a label transfer score cutoff of 0.3. If the cells are below this, they are marked as NA. These may represent cells missing from the reference.


It can be complex and time-consuming to analyze each sample individually before integration; however, the reward is often worth the effort. Characterizing each sample allows us to assess the presence of cell types in each sample carefully. For example, we may find that all the samples contain neuron cells, while only one sample may contain macrophage cells, but other samples do not. This is useful for interpreting the downstream results of differential expression across samples; for example, it would be wise to focus on a comparative analysis of the cell types in all samples (neurons), and simply note that macrophages are present in some but missing in other samples.

Additionally, it is also useful to observe how the cell types are related to one another in the individual samples. We may observe that immune cell types like T cells and B cells may cluster closely together, while very different cell types, such as neurons and endothelial cells, may cluster far apart. Identifying such clustering patterns in individual samples may help identify cases of "over-integration" or other issues.
'
  
  cat(text)
  
  
}

6 Individual sample label transfer cell type predictions

For each sample we perform label transfer from a reference single-cell RNA-seq dataset. Here, we plot the results of the label transfer. Using a prior single-cell RNAseq dataset, we learn which cell types are present in the current data.

Using label transfer, we can get a score for each cell type in the reference dataset. A high score indicates a strong and confident match, while a low score indicates a match of low confidence and may represent a cell type that was not captured in the reference.

Note that we apply a label transfer score cutoff of 0.3. If the cells are below this, they are marked as NA. These may represent cells missing from the reference.

It can be complex and time-consuming to analyze each sample individually before integration; however, the reward is often worth the effort. Characterizing each sample allows us to assess the presence of cell types in each sample carefully. For example, we may find that all the samples contain neuron cells, while only one sample may contain macrophage cells, but other samples do not. This is useful for interpreting the downstream results of differential expression across samples; for example, it would be wise to focus on a comparative analysis of the cell types in all samples (neurons), and simply note that macrophages are present in some but missing in other samples.

Additionally, it is also useful to observe how the cell types are related to one another in the individual samples. We may observe that immune cell types like T cells and B cells may cluster closely together, while very different cell types, such as neurons and endothelial cells, may cluster far apart. Identifying such clustering patterns in individual samples may help identify cases of “over-integration” or other issues.

if(use_labeltransfer == T){
  
  #print to report
  
  # do this in a way that creates a section for each sample...
  # https://stackoverflow.com/questions/36674824/use-loop-to-generate-section-of-text-in-rmarkdown
  
  template_samples <- "


## Sample %s


"
  
  
  template_plots <- "


### %s


"
  
  template_celltypes <- "


#### %s


"
  
  
  for (i in 1:length( names(ctplots_individualsamples) ) ) {
    sampname <- names(ctplots_individualsamples)[i]
    
    cat(sprintf(template_samples, sampname))
    
    
    ctplotlist <- ctplots_individualsamples[[i]]
    
    #print them, with titles
    
    
    plotlab <- "


### Heatmap of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters.


"
    
    cat( plotlab )
    print(ctplotlist$hm_ctscores)
    
    
    
    plotlab <- "


### Dotplot of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters. It is very similar to the heatmap above. 

However, the dotplot also includes information about how many cells in the cluster express the cell type score. A big dot indicates many cells in that cluster express it at non-zero level. A big gray dot indicates widespread, low score among cells. A big red dot indicates widespread, high score among cells. A blue dot indicates relatively low score. A small dot indicates few cells express the score.


"
    
    cat( plotlab )
    
    print(ctplotlist$dp_ctscores)
    
    
    
    
    
    plotlab <- "


### Heatmap of reference cell type markers

Here we plot the top 5 reference celltype markers as sorted by average log2 fold change. The markers are derived from the reference dataset. Since these are the top specific markers of each cell type in the reference, their expression pattern should match clusters with a high label transfer score for each given cell type. For example, a cluster with high T cell score should highly express T cell markers.



"
    
    cat( plotlab )
    
    print(ctplotlist$hm_refmarkers)
    
    
    
    plotlab <- "


### Heatmap of reference cell type markers averaged across clusters

Here we plot the top 5 reference cell type markers by average log2 fold change, this time using the average cluster expression. The markers are derived from the reference dataset. Using averages allows us to easily see which clusters highly or lowly express the reference cell type markers.




"
    
    cat( plotlab )
    
    print(ctplotlist$hm_refmarkers_avg)
    
    
    
    
    
    plotlab <- "


### UMAP of top scoring celltypes

The top scoring celltype for each cell is plotted on the UMAP.



"
    
    cat( plotlab )
    
    print(ctplotlist$d2_a)
    
    
    plotlab <- "


### UMAP of label transfer scores

The scores of the cell types are plotted on the UMAP. This shows how confident the prediction is. If the score is low, it may be that the cells present in this dataset are missing from the reference.


"
    
    cat( plotlab )
    
    
    
    print(ctplotlist$d2_b)
    
    
    
    plotlab <- "


### Alluvial plot mapping between Louvain clusters to top celltypes

This alluvial plot maps cluster labels to top celltype labels for each cell. This can be useful to identify general trends of celltype score per cluster.
The top-scoring cell type is plotted, but some cells and clusters may contain a mix of cell types.



"
    
    cat( plotlab )
    
    
    print(ctplotlist$ap)
    
    
    
    plotlab <- '


### Table showing cluster-celltype mapping

We apply an ad-hoc method to extend the label transfer from individual cells (default) across entire clusters. We do this by taking the average score of each cell type score in the cluster and choosing the max. This can help simplify the analysis and can be less noisy by sharing information across cells, but may also hide intra-cluster variability, such as particular sub-clusters which may correspond to distinct cell sub-types.

We also apply a thresholding approach such that clusters with a max score of less than 0.3 are considered non-transferrable and marked as NA (not applicable). These may reflect clusters of cells which are not in the reference dataset.
Additionally, clusters with a score between 0.3 - 0.5 are marked with the suffix "_putative" to indicate the somewhat uncertain status. All cluster markers should be carefully inspected and checked for cell type, independnetly of the label transfer results.

'
    
    cat( plotlab )
    
    clustmaxprint <- ctplotlist$clustmaxdf
    clustmaxprint$score <- round(clustmaxprint$score, digits = 5)
    print(knitr::kable(clustmaxprint))
    
    
    
    plotlab <- "


### UMAP of cluster-celltype mapping

As described above, we extend the label transfer prediction from individual cells to whole clusters in order to denoise and simplify the analysis. Here, rather than plotting the individual cell predictions, we plot the cluster-level predictions.



"
    
    cat( plotlab )
    
    
    print(ctplotlist$d3)
    
    
    
    plotlab <- "


### Violin plots for each cell type

Here we plot a violin plot for each cell type to analyze the label transfer scores type in detail.


"
    
    cat( plotlab )
    
    for(j in 1:length(ctplotlist$ct_plots) ){
      
      ctname <- names(ctplotlist$ct_plots[j])
      
      cat(sprintf(template_celltypes, ctname))
      
      print( ctplotlist$ct_plots[[j]] )
      
    }
    
    
    
    
    
    
  }
  
  
  
}

6.1 Sample Healthy_1

6.1.1 Heatmap of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters.

6.1.2 Dotplot of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters. It is very similar to the heatmap above.

However, the dotplot also includes information about how many cells in the cluster express the cell type score. A big dot indicates many cells in that cluster express it at non-zero level. A big gray dot indicates widespread, low score among cells. A big red dot indicates widespread, high score among cells. A blue dot indicates relatively low score. A small dot indicates few cells express the score.

6.1.3 Heatmap of reference cell type markers

Here we plot the top 5 reference celltype markers as sorted by average log2 fold change. The markers are derived from the reference dataset. Since these are the top specific markers of each cell type in the reference, their expression pattern should match clusters with a high label transfer score for each given cell type. For example, a cluster with high T cell score should highly express T cell markers.

6.1.4 Heatmap of reference cell type markers averaged across clusters

Here we plot the top 5 reference cell type markers by average log2 fold change, this time using the average cluster expression. The markers are derived from the reference dataset. Using averages allows us to easily see which clusters highly or lowly express the reference cell type markers.

6.1.5 UMAP of top scoring celltypes

The top scoring celltype for each cell is plotted on the UMAP.

6.1.6 UMAP of label transfer scores

The scores of the cell types are plotted on the UMAP. This shows how confident the prediction is. If the score is low, it may be that the cells present in this dataset are missing from the reference.

6.1.7 Alluvial plot mapping between Louvain clusters to top celltypes

This alluvial plot maps cluster labels to top celltype labels for each cell. This can be useful to identify general trends of celltype score per cluster. The top-scoring cell type is plotted, but some cells and clusters may contain a mix of cell types.

6.1.8 Table showing cluster-celltype mapping

We apply an ad-hoc method to extend the label transfer from individual cells (default) across entire clusters. We do this by taking the average score of each cell type score in the cluster and choosing the max. This can help simplify the analysis and can be less noisy by sharing information across cells, but may also hide intra-cluster variability, such as particular sub-clusters which may correspond to distinct cell sub-types.

We also apply a thresholding approach such that clusters with a max score of less than 0.3 are considered non-transferrable and marked as NA (not applicable). These may reflect clusters of cells which are not in the reference dataset. Additionally, clusters with a score between 0.3 - 0.5 are marked with the suffix “_putative” to indicate the somewhat uncertain status. All cluster markers should be carefully inspected and checked for cell type, independnetly of the label transfer results.

cluster	max	score	label
0	NK	0.96341	NK
1	MNP	1.00000	MNP
2	T	0.99916	T
3	B	0.97787	B
4	T	0.91109	T
5	B	1.00000	B
6	MNP	1.00000	MNP

6.1.9 UMAP of cluster-celltype mapping

As described above, we extend the label transfer prediction from individual cells to whole clusters in order to denoise and simplify the analysis. Here, rather than plotting the individual cell predictions, we plot the cluster-level predictions.

6.1.10 Violin plots for each cell type

Here we plot a violin plot for each cell type to analyze the label transfer scores type in detail.

6.1.10.1 MNP

6.1.10.2 B

6.1.10.3 NK

6.1.10.4 T

6.2 Sample Healthy_2

6.2.1 Heatmap of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters.

6.2.2 Dotplot of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters. It is very similar to the heatmap above.

However, the dotplot also includes information about how many cells in the cluster express the cell type score. A big dot indicates many cells in that cluster express it at non-zero level. A big gray dot indicates widespread, low score among cells. A big red dot indicates widespread, high score among cells. A blue dot indicates relatively low score. A small dot indicates few cells express the score.

6.2.3 Heatmap of reference cell type markers

Here we plot the top 5 reference celltype markers as sorted by average log2 fold change. The markers are derived from the reference dataset. Since these are the top specific markers of each cell type in the reference, their expression pattern should match clusters with a high label transfer score for each given cell type. For example, a cluster with high T cell score should highly express T cell markers.

6.2.4 Heatmap of reference cell type markers averaged across clusters

Here we plot the top 5 reference cell type markers by average log2 fold change, this time using the average cluster expression. The markers are derived from the reference dataset. Using averages allows us to easily see which clusters highly or lowly express the reference cell type markers.

6.2.5 UMAP of top scoring celltypes

The top scoring celltype for each cell is plotted on the UMAP.

6.2.6 UMAP of label transfer scores

The scores of the cell types are plotted on the UMAP. This shows how confident the prediction is. If the score is low, it may be that the cells present in this dataset are missing from the reference.

6.2.7 Alluvial plot mapping between Louvain clusters to top celltypes

This alluvial plot maps cluster labels to top celltype labels for each cell. This can be useful to identify general trends of celltype score per cluster. The top-scoring cell type is plotted, but some cells and clusters may contain a mix of cell types.

6.2.8 Table showing cluster-celltype mapping

We apply an ad-hoc method to extend the label transfer from individual cells (default) across entire clusters. We do this by taking the average score of each cell type score in the cluster and choosing the max. This can help simplify the analysis and can be less noisy by sharing information across cells, but may also hide intra-cluster variability, such as particular sub-clusters which may correspond to distinct cell sub-types.

We also apply a thresholding approach such that clusters with a max score of less than 0.3 are considered non-transferrable and marked as NA (not applicable). These may reflect clusters of cells which are not in the reference dataset. Additionally, clusters with a score between 0.3 - 0.5 are marked with the suffix “_putative” to indicate the somewhat uncertain status. All cluster markers should be carefully inspected and checked for cell type, independnetly of the label transfer results.

cluster	max	score	label
0	NK	0.97930	NK
1	MNP	1.00000	MNP
2	T	1.00000	T
3	B	0.97849	B
4	B	0.99921	B
5	T	0.99633	T
6	MNP	1.00000	MNP

6.2.9 UMAP of cluster-celltype mapping

As described above, we extend the label transfer prediction from individual cells to whole clusters in order to denoise and simplify the analysis. Here, rather than plotting the individual cell predictions, we plot the cluster-level predictions.

6.2.10 Violin plots for each cell type

Here we plot a violin plot for each cell type to analyze the label transfer scores type in detail.

6.2.10.1 NK

6.2.10.2 T

6.2.10.3 B

6.2.10.4 MNP

6.3 Sample Covid_Mild_1

6.3.1 Heatmap of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters.

6.3.2 Dotplot of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters. It is very similar to the heatmap above.

However, the dotplot also includes information about how many cells in the cluster express the cell type score. A big dot indicates many cells in that cluster express it at non-zero level. A big gray dot indicates widespread, low score among cells. A big red dot indicates widespread, high score among cells. A blue dot indicates relatively low score. A small dot indicates few cells express the score.

6.3.3 Heatmap of reference cell type markers

Here we plot the top 5 reference celltype markers as sorted by average log2 fold change. The markers are derived from the reference dataset. Since these are the top specific markers of each cell type in the reference, their expression pattern should match clusters with a high label transfer score for each given cell type. For example, a cluster with high T cell score should highly express T cell markers.

6.3.4 Heatmap of reference cell type markers averaged across clusters

Here we plot the top 5 reference cell type markers by average log2 fold change, this time using the average cluster expression. The markers are derived from the reference dataset. Using averages allows us to easily see which clusters highly or lowly express the reference cell type markers.

6.3.5 UMAP of top scoring celltypes

The top scoring celltype for each cell is plotted on the UMAP.

6.3.6 UMAP of label transfer scores

The scores of the cell types are plotted on the UMAP. This shows how confident the prediction is. If the score is low, it may be that the cells present in this dataset are missing from the reference.

6.3.7 Alluvial plot mapping between Louvain clusters to top celltypes

This alluvial plot maps cluster labels to top celltype labels for each cell. This can be useful to identify general trends of celltype score per cluster. The top-scoring cell type is plotted, but some cells and clusters may contain a mix of cell types.

6.3.8 Table showing cluster-celltype mapping

We apply an ad-hoc method to extend the label transfer from individual cells (default) across entire clusters. We do this by taking the average score of each cell type score in the cluster and choosing the max. This can help simplify the analysis and can be less noisy by sharing information across cells, but may also hide intra-cluster variability, such as particular sub-clusters which may correspond to distinct cell sub-types.

We also apply a thresholding approach such that clusters with a max score of less than 0.3 are considered non-transferrable and marked as NA (not applicable). These may reflect clusters of cells which are not in the reference dataset. Additionally, clusters with a score between 0.3 - 0.5 are marked with the suffix “_putative” to indicate the somewhat uncertain status. All cluster markers should be carefully inspected and checked for cell type, independnetly of the label transfer results.

cluster	max	score	label
0	B	0.98125	B
1	NK	0.98922	NK
2	MNP	0.99991	MNP
3	T	0.99811	T
4	T	0.98932	T
5	NK	0.99483	NK

6.3.9 UMAP of cluster-celltype mapping

As described above, we extend the label transfer prediction from individual cells to whole clusters in order to denoise and simplify the analysis. Here, rather than plotting the individual cell predictions, we plot the cluster-level predictions.

6.3.10 Violin plots for each cell type

Here we plot a violin plot for each cell type to analyze the label transfer scores type in detail.

6.3.10.1 NK

6.3.10.2 B

6.3.10.3 MNP

6.3.10.4 T

6.4 Sample Covid_Mild_2

6.4.1 Heatmap of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters.

6.4.2 Dotplot of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters. It is very similar to the heatmap above.

However, the dotplot also includes information about how many cells in the cluster express the cell type score. A big dot indicates many cells in that cluster express it at non-zero level. A big gray dot indicates widespread, low score among cells. A big red dot indicates widespread, high score among cells. A blue dot indicates relatively low score. A small dot indicates few cells express the score.

6.4.3 Heatmap of reference cell type markers

Here we plot the top 5 reference celltype markers as sorted by average log2 fold change. The markers are derived from the reference dataset. Since these are the top specific markers of each cell type in the reference, their expression pattern should match clusters with a high label transfer score for each given cell type. For example, a cluster with high T cell score should highly express T cell markers.

6.4.4 Heatmap of reference cell type markers averaged across clusters

Here we plot the top 5 reference cell type markers by average log2 fold change, this time using the average cluster expression. The markers are derived from the reference dataset. Using averages allows us to easily see which clusters highly or lowly express the reference cell type markers.

6.4.5 UMAP of top scoring celltypes

The top scoring celltype for each cell is plotted on the UMAP.

6.4.6 UMAP of label transfer scores

The scores of the cell types are plotted on the UMAP. This shows how confident the prediction is. If the score is low, it may be that the cells present in this dataset are missing from the reference.

6.4.7 Alluvial plot mapping between Louvain clusters to top celltypes

This alluvial plot maps cluster labels to top celltype labels for each cell. This can be useful to identify general trends of celltype score per cluster. The top-scoring cell type is plotted, but some cells and clusters may contain a mix of cell types.

6.4.8 Table showing cluster-celltype mapping

We apply an ad-hoc method to extend the label transfer from individual cells (default) across entire clusters. We do this by taking the average score of each cell type score in the cluster and choosing the max. This can help simplify the analysis and can be less noisy by sharing information across cells, but may also hide intra-cluster variability, such as particular sub-clusters which may correspond to distinct cell sub-types.

We also apply a thresholding approach such that clusters with a max score of less than 0.3 are considered non-transferrable and marked as NA (not applicable). These may reflect clusters of cells which are not in the reference dataset. Additionally, clusters with a score between 0.3 - 0.5 are marked with the suffix “_putative” to indicate the somewhat uncertain status. All cluster markers should be carefully inspected and checked for cell type, independnetly of the label transfer results.

cluster	max	score	label
0	NK	0.88159	NK
1	MNP	1.00000	MNP
2	B	0.96980	B
3	T	0.99507	T
4	T	0.93617	T

6.4.9 UMAP of cluster-celltype mapping

As described above, we extend the label transfer prediction from individual cells to whole clusters in order to denoise and simplify the analysis. Here, rather than plotting the individual cell predictions, we plot the cluster-level predictions.

6.4.10 Violin plots for each cell type

Here we plot a violin plot for each cell type to analyze the label transfer scores type in detail.

6.4.10.1 NK

6.4.10.2 B

6.4.10.3 MNP

6.4.10.4 T

6.5 Sample Covid_Critical_1

6.5.1 Heatmap of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters.

6.5.2 Dotplot of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters. It is very similar to the heatmap above.

However, the dotplot also includes information about how many cells in the cluster express the cell type score. A big dot indicates many cells in that cluster express it at non-zero level. A big gray dot indicates widespread, low score among cells. A big red dot indicates widespread, high score among cells. A blue dot indicates relatively low score. A small dot indicates few cells express the score.

6.5.3 Heatmap of reference cell type markers

Here we plot the top 5 reference celltype markers as sorted by average log2 fold change. The markers are derived from the reference dataset. Since these are the top specific markers of each cell type in the reference, their expression pattern should match clusters with a high label transfer score for each given cell type. For example, a cluster with high T cell score should highly express T cell markers.

6.5.4 Heatmap of reference cell type markers averaged across clusters

Here we plot the top 5 reference cell type markers by average log2 fold change, this time using the average cluster expression. The markers are derived from the reference dataset. Using averages allows us to easily see which clusters highly or lowly express the reference cell type markers.

6.5.5 UMAP of top scoring celltypes

The top scoring celltype for each cell is plotted on the UMAP.

6.5.6 UMAP of label transfer scores

The scores of the cell types are plotted on the UMAP. This shows how confident the prediction is. If the score is low, it may be that the cells present in this dataset are missing from the reference.

6.5.7 Alluvial plot mapping between Louvain clusters to top celltypes

This alluvial plot maps cluster labels to top celltype labels for each cell. This can be useful to identify general trends of celltype score per cluster. The top-scoring cell type is plotted, but some cells and clusters may contain a mix of cell types.

6.5.8 Table showing cluster-celltype mapping

We apply an ad-hoc method to extend the label transfer from individual cells (default) across entire clusters. We do this by taking the average score of each cell type score in the cluster and choosing the max. This can help simplify the analysis and can be less noisy by sharing information across cells, but may also hide intra-cluster variability, such as particular sub-clusters which may correspond to distinct cell sub-types.

We also apply a thresholding approach such that clusters with a max score of less than 0.3 are considered non-transferrable and marked as NA (not applicable). These may reflect clusters of cells which are not in the reference dataset. Additionally, clusters with a score between 0.3 - 0.5 are marked with the suffix “_putative” to indicate the somewhat uncertain status. All cluster markers should be carefully inspected and checked for cell type, independnetly of the label transfer results.

cluster	max	score	label
0	T	0.99916	T
1	MNP	1.00000	MNP
2	B	0.99378	B
3	NK	0.86349	NK
4	B	0.99557	B

6.5.9 UMAP of cluster-celltype mapping

As described above, we extend the label transfer prediction from individual cells to whole clusters in order to denoise and simplify the analysis. Here, rather than plotting the individual cell predictions, we plot the cluster-level predictions.

6.5.10 Violin plots for each cell type

Here we plot a violin plot for each cell type to analyze the label transfer scores type in detail.

6.5.10.1 MNP

6.5.10.2 T

6.5.10.3 NK

6.5.10.4 B

6.6 Sample Covid_Critical_2

6.6.1 Heatmap of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters.

6.6.2 Dotplot of label transfer cell type scores

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters. It is very similar to the heatmap above.

However, the dotplot also includes information about how many cells in the cluster express the cell type score. A big dot indicates many cells in that cluster express it at non-zero level. A big gray dot indicates widespread, low score among cells. A big red dot indicates widespread, high score among cells. A blue dot indicates relatively low score. A small dot indicates few cells express the score.

6.6.3 Heatmap of reference cell type markers

Here we plot the top 5 reference celltype markers as sorted by average log2 fold change. The markers are derived from the reference dataset. Since these are the top specific markers of each cell type in the reference, their expression pattern should match clusters with a high label transfer score for each given cell type. For example, a cluster with high T cell score should highly express T cell markers.

6.6.4 Heatmap of reference cell type markers averaged across clusters

Here we plot the top 5 reference cell type markers by average log2 fold change, this time using the average cluster expression. The markers are derived from the reference dataset. Using averages allows us to easily see which clusters highly or lowly express the reference cell type markers.

6.6.5 UMAP of top scoring celltypes

The top scoring celltype for each cell is plotted on the UMAP.

6.6.6 UMAP of label transfer scores

The scores of the cell types are plotted on the UMAP. This shows how confident the prediction is. If the score is low, it may be that the cells present in this dataset are missing from the reference.

6.6.7 Alluvial plot mapping between Louvain clusters to top celltypes

This alluvial plot maps cluster labels to top celltype labels for each cell. This can be useful to identify general trends of celltype score per cluster. The top-scoring cell type is plotted, but some cells and clusters may contain a mix of cell types.

6.6.8 Table showing cluster-celltype mapping

We apply an ad-hoc method to extend the label transfer from individual cells (default) across entire clusters. We do this by taking the average score of each cell type score in the cluster and choosing the max. This can help simplify the analysis and can be less noisy by sharing information across cells, but may also hide intra-cluster variability, such as particular sub-clusters which may correspond to distinct cell sub-types.

We also apply a thresholding approach such that clusters with a max score of less than 0.3 are considered non-transferrable and marked as NA (not applicable). These may reflect clusters of cells which are not in the reference dataset. Additionally, clusters with a score between 0.3 - 0.5 are marked with the suffix “_putative” to indicate the somewhat uncertain status. All cluster markers should be carefully inspected and checked for cell type, independnetly of the label transfer results.

cluster	max	score	label
0	B	1.00000	B
1	MNP	1.00000	MNP
2	T	0.99799	T
3	NK	0.96310	NK
4	NK	0.63160	NK
5	T	0.98930	T

6.6.9 UMAP of cluster-celltype mapping

As described above, we extend the label transfer prediction from individual cells to whole clusters in order to denoise and simplify the analysis. Here, rather than plotting the individual cell predictions, we plot the cluster-level predictions.

6.6.10 Violin plots for each cell type

Here we plot a violin plot for each cell type to analyze the label transfer scores type in detail.

6.6.10.1 MNP

6.6.10.2 B

6.6.10.3 NK

6.6.10.4 T

7 Integration of samples

Here, we combine all of the samples into a single unified dataset.

“Integration” refers to the process of combining together the data from individual samples with batch correction. Typically, library prep is done one sample at a time, which may introduce bias due to technical artifacts associated with processing for that sample, i.e., particular reagent lot used, slightly longer incubation periods, slightly more reads sequenced, etc. To combine samples, we thus apply special batch-correction procedures. The method for doing that here relies on Reference Principal Component Integration, implemented in the RISC package (Liu, Zheng et al Nat Biotech 2021). This approach exploits the fact that cells from the same type typically have similar transcriptomic patterns, and then tries to match the patterns.

Ultimately, the goal of integration is to minimize technical noise and maximize shared biological signals, the strongest of which is typically cell type.

Integration can be contrasted with “concatenation,” which involves simply merging the samples without any batch correction. If there are strong batch effects in the data, this method may result in clusters driven not by cell type but by the sample of origin (i.e., cluster 1 is all cells from sample 1, cluster 2 is all cells from sample 2).

# before integrating, add a few important columns to each sobj metadata
# "sample" column --> switch to Code
# "condition" column
# barcode column


#add sample to each sobj
codes <- sample_metadata$Code

sobjlist <- lapply(codes, function(code){
  
  sample_metadata_code <- sample_metadata[sample_metadata$Code == code,]
  
  sobj <- sobjlist[[code]]
  sobj$orig.ident <- code
  sobj$Code <- code
  sobj$Sample <- sample_metadata_code$Sample
  sobj
})

names(sobjlist) <- codes


#add a condition column
sobjlist <- lapply(names(sobjlist), function(sampname){
  
  cond <- sample_metadata[sample_metadata$Code == sampname,"Condition"]
  
  sobj <- sobjlist[[sampname]]
  sobj$Condition <- cond
  sobj
  
})

names(sobjlist) <- codes


#add a barcode column
sobjlist <- lapply(names(sobjlist), function(sampname){
  
  
  sobj <- sobjlist[[sampname]]
  md <- sobj@meta.data
  md <- cbind(rownames(md), md)
  colnames(md)[1] <- 'Barcode'
  sobj@meta.data <- md
  sobj
  
  
})

names(sobjlist) <- codes





# clean up env before doing this
rm(ctplotlist, ctplots_individualsamples, elbowplots, m_reference,
   cutoffplots, cutoffs, sampsumplots, 
   elbowplots,
   mlist_individualsamples_clusters, 
   qc_vln_feature, qc_vln_umi,
   summaryplots_individualsamples)



invisible(gc(full = T, reset = F, verbose = F))




#### save each object and get the metadata

#add clusters to each sobjlist object and then save each object
tmpobjdir <- paste0(outdir_indi, '/.tmp_Seurat_objects/')
dir.create(tmpobjdir, recursive = T)

mdlist <- lapply(sample_metadata$Code, function(code){
  
  sobj <- sobjlist[[code]]
  saveRDS(sobj, paste0(tmpobjdir, '/', code, '.rds'))
  
  md <- sobj@meta.data
  
  return(md)
  
})


names(mdlist) <- sample_metadata$Code



### qc purposes, read in  --> keep commented
# sobjlist <- lapply(sample_metadata$Code, function(code){
#   
#   message(code)
#   sobj <- readRDS( paste0(tmpobjdir, '/', code, '.rds') )
#   
#   sobj
#   
# })
# names(sobjlist) <- sample_metadata$Code



#remove gene list and seurat objects form env and purge
rm(sobjlist)
invisible(gc(full = T, reset = F, verbose = F))


#read in raw seurat objects, filter using mdlist
if(input_seurat_obj == T){
  
  matlist <- lapply(sample_metadata$Code, function(code){
    
    #get md for this object
    md <- mdlist[[code]]
    
    #read raw matrix
    samp <- sample_metadata[sample_metadata$Code == code,'Sample']
    
    #for HTO, we will assume saved objects
    sobjfile <- paste0(datadir, '/', samp, '.rds')
    sobj <- readRDS(sobjfile)
    
    #get RNA counts matrix
    # mat0 <- sobj@assays$RNA@counts
    mat0 <- GetAssayData(sobj, assay = 'RNA', layer = 'counts')
    
    
    #filter using sobj metadata
    mat0 <- mat0[,match(rownames(md), colnames(mat0))]
    
    
    return(mat0)
    
  })
} else{
  
  
  
  
  matlist <- lapply(sample_metadata$Code, function(code){
    
    #get md for this object
    md <- mdlist[[code]]
    
    #read raw matrix
    samp <- sample_metadata[sample_metadata$Code == code,'Sample']
    datafp <- paste0(datadir, '/', samp)
    # if on hpc, use below
    # datafp <- paste0(datadir, '/', samp, '/outs/')
    
    # for the dl data, we need to find the filepath
    h5_filename <- grep(pattern = 'filtered_feature_bc_matrix.h5',
                        list.files(datafp, recursive = T, full.names = T),
                        value = T)
    
    #get RNA counts matrix
    mat0 <- Read10X_h5(h5_filename)
    
    #filter using sobj metadata
    mat0 <- mat0[,match(rownames(md), colnames(mat0))]
    
    
    return(mat0)
    
  })
  
}

names(matlist) <- sample_metadata$Code

# keep intersect genes; will only remove genes if aligned with different GTFs
var0 <- Reduce(intersect, lapply(matlist, FUN = rownames))
matlist <- lapply(matlist, function(mat){ mat[match(var0, rownames(mat)), ] })
rm(var0)

#join matrices, filter genes from joint mat and proceed with union
bigmat <- do.call(cbind, matlist)

#get num cells expressing
num_nonzeros <- tabulate(bigmat@i + 1)

#get joint filtered genes as those exp by  >= 3 cells
joint_filt_genes <- rownames(bigmat)[num_nonzeros >= 3]



# prep the risc objects #
risclist <- lapply(sample_metadata$Code, function(code){
  
  #get md and mat0
  md <- mdlist[[code]]
  mat0 <- matlist[[code]]
  
  #subset matrix using jointly filtered genes
  mat0 <- mat0[rownames(mat0) %in% joint_filt_genes,]
  mat0 <- mat0[match(joint_filt_genes, rownames(mat0)),]
  
  
  #prep metadata for risc
  coldata0 <- md
  rm(md)
  
  #get barcodes, strip numeric suffix, and add samplename prefix
  barcodes <- stringr::str_split_fixed(rownames(coldata0), '-', 2)[,1]
  barcodes <- paste0(coldata0$orig.ident, '.',barcodes)
  
  coldata0 <- cbind(barcodes, coldata0)
  rm(barcodes)
  
  
  #make the rowdatadf...
  rowdata0 = data.frame(Symbol = rownames(mat0), row.names = rownames(mat0))
  
  #make the risc object
  ### make sure to set is.filter = F or it will still use sample-specific filtering
  dat0 = readsc(mat0, coldata0, rowdata0, is.filter = F)
  
  rm(mat0, rowdata0, coldata0)
  
  return(dat0)
})

names(risclist) <- sample_metadata$Code


## save matlist, we will add it to RNA assay later ##
outdir_int_objects <- paste0(outdir_int, '/data_objects/')
dir.create(outdir_int_objects, recursive = T)
saveRDS(matlist, paste0(outdir_int_objects, '/.concatmatrix.rds'))

#clean env
rm(bigmat, num_nonzeros, joint_filt_genes, matlist, mdlist)
invisible(gc(full = T, reset = F, verbose = F))






#### process each sample ####
# make sure to set ncore to 1 for all; we'll parallelize across samples for this

#prepare the RISC functions
process0 <- function(obj0){
  
  # Filter cells and genes
  message('scFilter')
  obj0 = RISC::scFilter(obj0, min.UMI = 0, max.UMI = Inf, min.gene = 0, min.cell = 0, is.filter = F)
  
  # Normalize the raw counts
  message('scNormalize')
  obj0 = RISC::scNormalize(obj0, ncore = 1)
  
  # Find highly variable genes
  message('scDisperse')
  obj0 = RISC::scDisperse(obj0)
  
  return(obj0)
}




### process each sample with RISC functions, in parallel
cl <- parallel::makeCluster(workernum)
doParallel::registerDoParallel(cl)

risclist <- foreach(dat0 = risclist,
                    .packages = c('RISC')) %dopar% 
  {
    return( process0(dat0) )
  }


parallel::stopCluster(cl)
invisible(gc(full = T, reset = F, verbose = F))
names(risclist) <- sample_metadata$Code



## variable genes - we still use all genes.
#get the intersect of gene names
var0 <- Reduce(intersect, lapply(risclist, FUN = function(x){x@rowdata$Symbol}))




#run inplot - just to show the plot later
#pdf null prevents premature plot showing
pdf( NULL )

ip <- InPlot(risclist, var.gene = var0, Std.cut = 0.95, ncore = workernum) 
ip <- patchwork::wrap_plots(ip)

dev.off()



### reference- either user-defined, or guess

if( !(is.null(risc_reference)) ){
  
  
  
  #write some text about which one was selected
  #cat()
  
  
  #if provided, we need the numeric index of which sample was given
  
  #it can be either sample or code name
  if(any(risc_reference %in% sample_metadata$Code)){
    ref <- grep(risc_reference, sample_metadata$Code)
  }
  
  #it can be either sample or code name
  if(any(risc_reference %in% sample_metadata$Sample)){
    ref <- grep(risc_reference, sample_metadata$Code)
  }
  
} 


#if not provided, still do automated guess but just use the provided one
# inplot is not automated and does not suggest which sample to use.
# instead, we use the one with most clusters, given that
# all samples were processed otherwise identically (same PCs and resolution)


# get num clusters from each sample
numclusts <- sapply(risclist, function(dat0){
  length(unique(dat0@coldata$seurat_clusters))
})

# get cell num score; higher cell num is prioritized
numcells_per_sample <- sapply(risclist, function(dat0){
  nrow(dat0@coldata)
})
numcells_per_sample <- numcells_per_sample / max(numcells_per_sample)

#multipy number of cluste4rs by cell num score
numclusts <- numclusts * numcells_per_sample

#average each cluster and get averaged variance?
# do NOT use pseudobulk, which adds up
pbvar <- sapply(risclist, function(dat0){
  
  mat <- dat0@assay$logcount
  md <- dat0@coldata
  
  #pseudobulk
  pb <- pseudobulk(obj = mat, metadata = md, grouping_colname_in_md = 'seurat_clusters')
  
  #average: divide pseudobulk columns by num cells
  numcells <- table(md$seurat_clusters)
  pb <- sweep(pb, 2, numcells, FUN = '/')
  
  clustervar <- apply(pb, 2, function(x){var(x)})
  
  mean(clustervar)
  
})

#multiply number of clusts * average of cluster variance
refscore <- numclusts * pbvar



### try to divide by KS? ###



#autoselect ref
maxautoscore <- which.max(refscore)

# set reference as autoselected; only if not provided
if( is.null(risc_reference) ){
  ref <- maxautoscore
}



#need to rearrange the list...
# the reference must be the first list element...
if(ref != 1){
  
  data0 <- list(risclist[[ref]])
  names(data0) <- names(risclist)[ref]
  
  for(i in 1:length(risclist)){
    if(i != ref){
      name = names(risclist)[i]
      data0[[name]] <- risclist[[i]]
    }
    
  }
  
} else{
  data0 <- risclist
}

rm(risclist)
invisible(gc(full = T, reset = F, verbose = F))

### actual integration ###
#set "eigens", num PCs to use for integration
eigens <- pcs_int

#actually integrate
data0 = scMultiIntegrate(
  objects = data0, eigens = eigens, add.Id = NULL, var.gene = var0,
  # method = "RPCI", 
  align = 'OLS', npc = 50, adjust = TRUE,
  ncore = workernum, 
  #do.fast = "AUTO"
)


rm(var0)
invisible(gc(full = T, reset = F, verbose = F))

# integrated UMAP
data0 = scUMAP(data0, npc = eigens, use = "PLS")

#### integrated clustering ####
### AS OF JUL 5 2023 USE scDAPP::scCluster_louvain_res()
# # integrated clustering
# # neighbor = 10 is default... maybe make it a variable too...?
# data0 <- scCluster(data0, 
#                    slot = "cell.pls",
#                    method = 'louvain',
#                    npc = eigens, 
#                    neighbor = 10
# )
# 
# ### remap clust, biggest to smallest...
# #get risc clust
# rc <- data0@coldata$Cluster
# 
# #sort by biggest to smallest
# bs <- sort(table(rc), decreasing = T)
# rc <- plyr::mapvalues(rc, from = names(bs), to = c(1:length(bs)) )
# rc <- factor(rc, levels = 1:length(bs))
# 
# #set to object
# data0@coldata$Cluster <- rc
# rm(rc,bs)

data0 = scDAPP::scCluster_louvain_res(object = data0,
                                    slot = "cell.pls",
                                    # method = 'louvain',
                                    npc = eigens,
                                    resolution = res_int,
                                    neighbor = RISC_louvain_neighbors,
)







#save it
outdir_int_objects <- paste0(outdir_int, '/data_objects/')
dir.create(outdir_int_objects)
saveRDS(data0, paste0(outdir_int_objects, '/RISC-object_integrated.rds'))



#cluster markers --> do this with seurat instead
# riscmarkertime <- proc.time()
# risc_clustermarkers = AllMarker(data0, ncore = workernum)
# riscmarkertime <- proc.time() - riscmarkertime
# takes about an hour, even with parallelization

#get matrix, umap, md, pca, etc; then put into seurat object

mat <- do.call(cbind, data0@assay$logcount)
md <- data0@coldata
umap <- data0@DimReduction$cell.umap
pca <- data0@DimReduction$cell.pls


#rename "Cluster" to "RISC cluster"
risc_clust_lab <- paste0('RISC_Louvain_npc', pcs_int, '_res', res_int)
colnames(md)[ncol(md)] <- risc_clust_lab
clustname <- risc_clust_lab

#make obj
sobjint <- CreateSeuratObject(counts = CreateAssayObject(data=mat),
                              assay = 'RISC', project = 'Integrated',
                              meta.data = md
)

#add dim reducs
sobjint[['umap']] <-  CreateDimReducObject(umap, assay = 'RISC', key = 'UMAP_')
sobjint[['pca']] <-  CreateDimReducObject(pca, assay = 'RISC', key = 'PCA_')





#clean env
rm(umap,pca,mat,md, data0)
invisible(gc(full = T, reset = F, verbose = F))

### find markers ###

#set default levels
sobjint <- SetIdent(sobjint, value = sobjint@meta.data[,risc_clust_lab])
sobjint$seurat_clusters <- sobjint@meta.data[,risc_clust_lab]


#save object
saveRDS(sobjint, paste0(outdir_int_objects, '/Seurat-object_integrated.rds'))


#save markers
intmarkersdir <- paste0(outdir_int, '/markergenes_intclusters/')
dir.create(intmarkersdir, recursive = T)
intmarkersfile <- paste0(intmarkersdir, '/intmarkers-npc', pcs_int, '-res', res_int,  '.csv')

#get markers, parallelized
# TURN OFF PAR FOR NOW, it seems to break things
# ALSO MAKE SURE NOT TO DO READ IN THING IN CASE OF CLUSTERING DIFFS
# future::plan('multisession', workers=workernum)

# if(!file.exists(intmarkersfile)){
#   
#   #Seurat clusters
#   seuratmarkertime <- proc.time()
# m_integrated_clusters <- FindAllMarkers(sobjint,
#                                         only.pos = T)
#   seuratmarkertime <- proc.time() - seuratmarkertime
#   
#   
#   # future::plan(strategy = 'sequential')
#   
#   # takes about an hour, future parallel is not stable and cause memory crash
#   
#   
# write.csv(m_integrated_clusters, intmarkersfile, quote = F, row.names = F)
#   
#   
# } else{
# m_integrated_clusters <- read.csv(intmarkersfile)
# 
# }


m_integrated_clusters <- FindAllMarkers(sobjint,
                                        only.pos = T)

# as of Nov 9 2023 (Seurat v5): add score to markers
m_integrated_clusters$score <- (m_integrated_clusters$pct.1 - m_integrated_clusters$pct.2) * m_integrated_clusters$avg_log2FC


write.csv(m_integrated_clusters, intmarkersfile, quote = F, row.names = F)

#remove sobjint for now, so we can read in each seurat object and save them.
# we'll read back in after
#add int clusters to each sobj in sobjlist with intmd
intmd <- sobjint@meta.data
rm(sobjint)
invisible(gc(full = T, reset = F, verbose = F))


## WE ALSO NEED PREDICTIONS ASSAY FROM INDIVIDUAL OBJECTS
# only if labeltransfer == T

#add clusters to each sobjlist object and then save each object
# shoudl already be created above...
outdir_indi_seuratobjs <- paste0(outdir_indi, '/processed_Seurat_objects/')
dir.create(outdir_indi_seuratobjs, recursive = T)

predictionmats <- lapply(sample_metadata$Code, function(code){
  
  #read in from temp dir
  sobj <- readRDS(paste0(tmpobjdir, '/', code, '.rds'))
  
  #subset intmd for this sample
  intmd_sub <- intmd[intmd$Code == code,]
  
  #get md for this sample from sobjlist (pre-int sobj)
  md <- sobj@meta.data
  
  #make sure they match (very likely they always will...?)
  intmd_sub <- intmd_sub[match(md$Barcode, intmd_sub$scBarcode),]
  
  #get clusters; clustname defined above
  sobj@meta.data[,clustname] <- intmd_sub[,clustname]
  
  #save each sample obj to proc obj dir
  invisible( saveRDS(sobj,
                     paste0(outdir_indi_seuratobjs, '/SeuratObject-', code, '.rds'))
  )
  
  if(use_labeltransfer == T){
    #return prediction assay with proper risc barcodes
    predictionmat <- sobj@assays$predictions@data
    colnames(predictionmat) <- intmd_sub$Barcode
    return(predictionmat)
  } else{
    return(code)
  }
  
})



#remove temp folder
unlink(tmpobjdir, recursive = T)



#read sobjint back in
sobjint <- readRDS( paste0(outdir_int_objects, '/Seurat-object_integrated.rds') )


#add predictions assay to integrated seurat object
if(use_labeltransfer==T){
  
  #combine prediction matrices and add to seurat obj
  predmat <- as(as.matrix(dplyr::bind_cols(predictionmats)), "sparseMatrix")
  rownames(predmat) <- rownames(predictionmats[[1]])
  
  #match order of barcodes
  predmat <- predmat[,match(rownames(intmd), colnames(predmat))]
  
  predassay <- CreateAssayObject(data=predmat)
  
  sobjint[['predictions']] <- predassay
  
  
  
  ### also predict int cluster-celltype mapping ###
  
  #get predmat
  predmat <- sobjint@assays$predictions@data
  
  #drop "max"
  predmat <- predmat[1:(nrow(predmat)-1), ]
  
  #for each cluster, get max
  md <- sobjint@meta.data
  clustname <- 'seurat_clusters'
  
  clustmaxlist <- lapply(levels(md[,clustname]), function(clust){
    clustcells <- rownames( md[md[,clustname] == clust,] )
    clustpred <- predmat[,colnames(predmat) %in% clustcells,drop=F]
    
    clust_avgscores <- Matrix::rowMeans(clustpred)
    maxscore <- clust_avgscores[which.max(clust_avgscores)]
    
    data.frame(cluster = clust, max = names(maxscore), score = maxscore, row.names = NULL)
    
  })
  
  clustmaxdf <- dplyr::bind_rows(clustmaxlist)
  
  
  #make a label
  clustmaxdf$label <- paste0( clustmaxdf$max)
  
  
  #uncertainty... if < 0.3, put as NA
  # if between 0.3 - 0.6, put putative
  clustmaxdf[clustmaxdf$score < 0.3,'label'] <- NA
  clustmaxdf[clustmaxdf$score >= 0.3 & clustmaxdf$score < 0.5,'label'] <- paste0( clustmaxdf[clustmaxdf$score >= 0.3 & clustmaxdf$score < 0.5,'label'],
                                                                                  '_putative')
  
  
  
  
  
  
  #add to metadata
  sobjint$INT_celltype_cluster_prediction <- sobjint$seurat_clusters
  sobjint$INT_celltype_cluster_prediction <- plyr::mapvalues(sobjint$INT_celltype_cluster_prediction,
                                                             from = levels(sobjint$INT_celltype_cluster_prediction),
                                                             to = clustmaxdf$label)
  
  
  
  #write out cluster-celltype mapping
  labeltransfer_outdir_int <- paste0(outdir_int, '/labeltransfer/')
  dir.create(labeltransfer_outdir_int)
  
  clustcelltypemapfile <- paste0(labeltransfer_outdir_int, '/IntClusterCelltypeMapping.csv')
  write.csv(x=clustmaxdf,
            file = clustcelltypemapfile, 
            row.names = F, quote = F)
  
  
  
  rm(predmat,predictionmats, predassay, clustmaxlist)
  
}




## clean up env
rm(predictionmats, intmd)

invisible(gc(full = T, reset = F, verbose = F))


## readin concat matrix with raw counts and put in seurat object ##
matlist <- readRDS( paste0(outdir_int_objects, '/.concatmatrix.rds') )

#for each matrix, change cell names to match seurat object
set_code <- paste0(sobjint$Set, '_', sobjint$Code)
set_code <- unique(set_code)
set_code <- str_split_fixed(set_code,pattern =  '_', n=2)
matlist <- lapply(1:length(matlist), function(i){
  mat <- matlist[[i]]
  code <- names(matlist)[i]
  set <- set_code[set_code[,2] == code,1]
  colnames(mat) <- paste0(set, '_', colnames(mat))
  mat
  
})

#join matrices, filter genes from joint mat and proceed with union
bigmat <- do.call(cbind, matlist)

#get num cells expressing
num_nonzeros <- tabulate(bigmat@i + 1)

#get joint filtered genes as those exp by  >= 3 cells
joint_filt_genes <- rownames(bigmat)[num_nonzeros >= 3]

# filter by these
bigmat <- bigmat[rownames(bigmat) %in% joint_filt_genes,]

# filter cells and match order to seurat
bigmat <- bigmat[,match(colnames(sobjint), colnames(bigmat))]
colnames(bigmat) <- colnames(sobjint)

#add as RNA assay
rnaassay <- Seurat::CreateAssayObject(counts = bigmat)
sobjint[['RNA']] <- rnaassay
DefaultAssay(sobjint) <- 'RNA'
sobjint <- NormalizeData(sobjint, assay = 'RNA')
DefaultAssay(sobjint) <- 'RISC'

#add in expm1 risc values to counts slot of risc assay
sobjint@assays$RISC@counts <- expm1(sobjint@assays$RISC@data)

#unlink temp concat mat
unlink( paste0(outdir_int_objects, '/.concatmatrix.rds') )

#clean env
invisible(gc(full = T, reset = F, verbose = F))
rm(bigmat, joint_filt_genes, num_nonzeros, matlist)




#factorize sample column to make sure it's the right order...
sobjint$Code <- factor(sobjint$Code, levels = sample_metadata$Code)

#factorize condiiton column to make sure it's the right order...
sobjint$Condition <- factor(sobjint$Condition, levels = levels(sample_metadata$Condition))


#update saved sobjint with predictions assay
saveRDS(sobjint,  paste0(outdir_int_objects, '/Seurat-object_integrated.rds') )


## clean up env
rm(predictionmats, intmd)

invisible(gc(full = T, reset = F, verbose = F))

7.1 Selection of reference sample for integration

RISC requires the selection of a reference sample from among the dataset. The optimal reference sample is one that has the most diversity of celltypes.

plotlab <- '
One way to select the reference sample is manual inspection of the "InPlot". This plot consists of three panels. The top panel is the most important, and shows the number of clusters detected in each sample across a range of different PC values. Essentially, this is a proxy of the number of cell types detected in the dataset, and the sample with most clusters should be selected as the reference. The second panel allows selection of the optimal PC value to use and the amount of variance each PC has for each sample, with good samples having a higher score (indicating more diversity). The third panel indicates bias of the gene signatures, samples with high scores here should not be selected as the reference.



'

cat(plotlab)

One way to select the reference sample is manual inspection of the “InPlot”. This plot consists of three panels. The top panel is the most important, and shows the number of clusters detected in each sample across a range of different PC values. Essentially, this is a proxy of the number of cell types detected in the dataset, and the sample with most clusters should be selected as the reference. The second panel allows selection of the optimal PC value to use and the amount of variance each PC has for each sample, with good samples having a higher score (indicating more diversity). The third panel indicates bias of the gene signatures, samples with high scores here should not be selected as the reference.

print(ip)

plotlab <- '

## Automated RISC reference selection

We have implemented an automated reference selection algorithm, extending the intuition laid out above. This is based on two metrics: 1) the number of clusters, and 2) the cluster-moderated sample variance. 

It is not enough to pick the sample with the most clusters, because sometimes samples have ties in the number of clusters, and cluster number alone can be proportional to number of cells. Thus, we weight the number or clusters by a metric of diversity in the sample which we call the cluster-moderated sample variance.

The cluster-moderated sample variance is calculated first by clustering each sample (ie, using the Seurat Louvain clusters described above), then averaging the sample at the cluster level. Then, the variance of each cluster is calculated, and finally, the mean of the cluster-wise variance is taken. 

Then, to calculate the reference selection score, we multiply the number of clusters per sample, times a relative weighing metric based on the number of cells in each sample relative to the sample with the most cells in the dataset (biggest gets a one, sampe with half cells of biggest gets a 0.5); then we multiply this value by the cluster-moderated variance described above.


We show here the resulting reference scores. The top score is selected as the reference. Please note that the autoselection is experimental, so if the score and selected reference deviates from InPlot, we recommend re-running the pipeline and manually selecting the optimal reference.

In this dataset, sample **%s** would be selected as the reference.


'

cat(sprintf(plotlab, names(maxautoscore)))

7.2 Automated RISC reference selection

We have implemented an automated reference selection algorithm, extending the intuition laid out above. This is based on two metrics: 1) the number of clusters, and 2) the cluster-moderated sample variance.

It is not enough to pick the sample with the most clusters, because sometimes samples have ties in the number of clusters, and cluster number alone can be proportional to number of cells. Thus, we weight the number or clusters by a metric of diversity in the sample which we call the cluster-moderated sample variance.

The cluster-moderated sample variance is calculated first by clustering each sample (ie, using the Seurat Louvain clusters described above), then averaging the sample at the cluster level. Then, the variance of each cluster is calculated, and finally, the mean of the cluster-wise variance is taken.

Then, to calculate the reference selection score, we multiply the number of clusters per sample, times a relative weighing metric based on the number of cells in each sample relative to the sample with the most cells in the dataset (biggest gets a one, sampe with half cells of biggest gets a 0.5); then we multiply this value by the cluster-moderated variance described above.

We show here the resulting reference scores. The top score is selected as the reference. Please note that the autoselection is experimental, so if the score and selected reference deviates from InPlot, we recommend re-running the pipeline and manually selecting the optimal reference.

In this dataset, sample Healthy_2 would be selected as the reference.

#format ref scores as data.frame
refscoredf <- data.frame('RefScore' = refscore)
refscoredf$Max <- ''
refscoredf[maxautoscore,'Max'] <- '*'
refscoredf$RefScore <- round(refscoredf$RefScore, 5)

#add "set"
refscoredf$Set <- paste0("Set-", 1:nrow(refscoredf))

#add sample and remove rownames
refscoredf$Code <- rownames(refscoredf)
rownames(refscoredf) <- NULL

#reorder
refscoredf <- refscoredf[,c('Code', 'Set', 'RefScore', 'Max')]


knitr::kable(refscoredf)

Code	Set	RefScore	Max
Healthy_1	Set-1	8.21950
Healthy_2	Set-2	9.31144	*
Covid_Mild_1	Set-3	6.81909
Covid_Mild_2	Set-4	5.03081
Covid_Critical_1	Set-5	5.24910
Covid_Critical_2	Set-6	7.78851

if( !(is.null(risc_reference)) ){
  
  plotlab <- '
## Manual reference selection: %s

In this analysis, sample %s was manually selected as the reference sample. Sample %s will be used as the reference, regardless of the auto-selection described above.

'
  
  cat(sprintf(plotlab, risc_reference, risc_reference, risc_reference))
  
}

#set summaryplot dir
outdir_int_plots <- paste0(outdir_int, '/integration_summaryplots/')
dir.create(outdir_int_plots)


#first, plot the ref selection stuff: inplot and autoselect

pdf( paste0(outdir_int_plots, 'RISC_reference_selection.pdf'), height = 8, width = 6  )
print(ip)
if( !is.null(risc_reference) ){
  print(
    pdftable(refscoredf, 
             title = paste0('Manual reference selected\nSample: ',
                            risc_reference)
    )
  )
} else{
  print(
    pdftable(refscoredf, 
             title = paste0('RISC reference\nautoselection')
    )
  )
}

dev.off()


## for each sample get int clusters for alluvial plot
mdint <- sobjint@meta.data


## plots of the integrated data

#umap of clusters
clustname <- risc_clust_lab
d1_int <- DimPlot(sobjint, label = T, repel = T, group.by = clustname)

#umap of samples
d2_int <- DimPlot(sobjint, group.by = 'Code', split.by = 'Condition', ncol = 2) + 
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.1))


#conditions
dcond <- DimPlot(sobjint, group.by = 'Condition')

#split by conditions
dcond_split <- DimPlot(sobjint, label = T, repel = T, 
                       group.by = clustname,
                       split.by = 'Condition',
                       ncol=2)+
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1.1))

#heatmap of cluster markers

DefaultAssay(sobjint) <- 'RISC'

n <- 5
top <- m_integrated_clusters %>% group_by(cluster) %>% top_n(n = n, wt = score)

genes <- top$gene

#scale the relevant genes
sobjint <- ScaleData(sobjint, features = genes, verbose = verbose)


# #make sure genes are in 
# if( any( !(genes %in% rownames(sobjint@assays$SCT@scale.data)) ) ){
#   
#   #try getresidual...
#   missinggenes <- genes[!(genes %in% rownames(sobjint@assays$SCT@scale.data))]
#   sobjint <- GetResidual(sobjint, missinggenes, na.rm = F, replace.value = T)
#   
#   #it can be complicated doing this after integration, some genes are NAs... 
#   scgem <- sobjint@assays$SCT@scale.data
#   
#   if( any( !complete.cases(scgem) ) ){
#     scgem <- scgem[complete.cases(scgem),]
#     top <- top[top$gene %in% rownames(scgem),]
#     sobjint@assays$SCT@scale.data <- scgem
#   }
#   rm(scgem)
#   
# }



#prep heatmap
top <- top[top$gene %in% rownames(sobjint),]
gem <- sobjint@assays$RISC@scale.data
gem <- gem[match(top$gene, rownames(gem)),]

#annot for clusters
#first order gem by cluster...
md <- sobjint@meta.data
md <- md[order(md$seurat_clusters),]
gem <- gem[,match(rownames(md), colnames(gem))]

clust_bc <- setNames(md$seurat_clusters,
                     nm = colnames(gem)
)

col_clust <- setNames(scales::hue_pal()(length(levels(sobjint$seurat_clusters))),
                      nm = levels(sobjint$seurat_clusters))

ha_clust <- ComplexHeatmap::HeatmapAnnotation(Cluster = clust_bc, col = list(Cluster = col_clust), show_legend = F)



#annot for markers

#set genes according to ct_ordered
top$cluster <-factor(top$cluster, levels=unique(top$cluster))
# top <- top[order(top$cluster),]
gem <- gem[match(top$gene, rownames(gem)),]

ct_gene <- setNames(top$cluster,
                    nm=top$gene)

col_gene <- col_clust
col_gene <- col_gene[names(col_gene) %in% top$cluster]

ha_genes <- ComplexHeatmap::rowAnnotation(Cluster = ct_gene, col = list(Cluster = col_gene),
                                          show_annotation_name=F)




#restrict range
gem[gem>5] <- 5
gem[gem<-5] <- -5



#actual heatmap
hm_int <- ComplexHeatmap::Heatmap(gem,
                                  # column_title = 'Integrated clusters',
                                  column_labels = rep('', ncol(gem)),
                                  row_names_gp = grid::gpar(fontsize = 5),
                                  column_split = md$seurat_clusters,
                                  row_split = top$cluster,
                                  row_title_gp = grid::gpar(fontsize = 5),
                                  row_gap = unit(0.8, "mm"), 
                                  column_gap = unit(0.8, "mm"),
                                  row_title_rot = 0,
                                  column_title_rot = 45,
                                  column_title_gp = grid::gpar(fontsize = 7),
                                  name = 'Scaled\nExpression',
                                  cluster_columns = F,
                                  cluster_rows = F,
                                  top_annotation = ha_clust,
                                  left_annotation = ha_genes,
                                  use_raster = F)




### marker avergae heatmap ###
#try to average the matrix
avgl <- lapply(levels(md$seurat_clusters), function(clust){
  mdc <- md[md$seurat_clusters==clust,]
  gemc <- gem[,colnames(gem) %in% rownames(mdc)]
  avg <- matrix(rowMeans(gemc), 
                dimnames = list(rownames(gem), clust))
  avg
})
avg <- do.call('cbind',avgl)

#need to re-prep column annot
clust_bc <- factor(str_sort(colnames(avg), numeric = T), levels = str_sort(colnames(avg), numeric = T))

col_clust <- setNames(scales::hue_pal()(length(levels(sobjint$seurat_clusters))),
                      nm = levels(sobjint$seurat_clusters))

ha_clust <- ComplexHeatmap::HeatmapAnnotation(Cluster = clust_bc, col = list(Cluster = col_clust), show_legend = F)


hm_int_avg <- ComplexHeatmap::Heatmap(avg,
                                      # column_title = 'Integrated clusters',
                                      row_names_gp = grid::gpar(fontsize = 6),
                                      column_names_gp = grid::gpar(fontsize = 6),
                                      column_split = factor(str_sort(colnames(avg), numeric = T), levels = str_sort(colnames(avg), numeric = T)), 
                                      column_title_rot = 0,
                                      column_names_rot = 0, 
                                      
                                      row_split = top$cluster,
                                      row_title_gp = grid::gpar(fontsize = 6),
                                      row_gap = unit(0.5, "mm"), 
                                      column_gap = unit(0.5, "mm"),
                                      row_title_rot = 0,
                                      name = 'Scaled\nExpression',
                                      cluster_columns = F,
                                      cluster_rows = F,
                                      top_annotation = ha_clust,
                                      left_annotation = ha_genes,
                                      use_raster = F)




## update Aug 8 add qc plots

int_qc_vln <- wrap_plots(ncol = 2, list(
  VlnPlot(sobjint, 'nCount_RNA', pt.size = 0.1)+NoLegend() ,
  VlnPlot(sobjint, 'nFeature_RNA', pt.size = 0.1)+NoLegend() ,
  VlnPlot(sobjint, 'percent.mito', pt.size = 0.1)+NoLegend() ,
  VlnPlot(sobjint, 'percent.hemoglobin', pt.size = 0.1)+NoLegend()
))









# DefaultAssay(sobjint) <- 'integrated'





### alluvial plot of condition to cluster

md <- sobjint@meta.data

labelsdf <- md[,c("Condition", clustname)]

ap_cond_to_clust <- alluvialplot(labelsdf)


### alluvial plot of sample to cluster
# do this for each condition separately

mdint <- sobjint@meta.data
ap_samp_to_clust_list <- lapply(unique(sample_metadata$Condition), function(cond){
  
  md <- mdint[mdint$Condition == cond,]
  
  
  labelsdf <- md[,c("Code", clustname)]
  #avoid relevlling, same colors for all gets confusing
  # levels(labelsdf$Code) <- levels(labelsdf$Code)[levels(labelsdf$Code) %in% labelsdf$Code]
  # labelsdf$Code <- factor(labelsdf$Code, levels = levels(labelsdf$Code)[levels(labelsdf$Code) %in% labelsdf$Code] )
  
  ap_samp_to_clust <- alluvialplot(labelsdf) + labs(title = cond) + NoLegend()
  
  return(ap_samp_to_clust)
  
})
names(ap_samp_to_clust_list) <- unique(sample_metadata$Condition)
ap_samp_to_clust <- wrap_plots(ap_samp_to_clust_list)





#loop thru each code, make alluvialplot of indi to int cluster
mdint <- sobjint@meta.data
indi_int_clust_ap_list <- lapply(sample_metadata$Code, function(code){
  md <- mdint[mdint$Code == code,]
  
  indiclustname <- paste0("SCT_snn_res.", res_indi)
  intclustname <- risc_clust_lab <- paste0('RISC_Louvain_npc', pcs_int, '_res', res_int)
  labelsdf <- md[,c(indiclustname, intclustname)]
  
  labelsdf[,1] <- factor(labelsdf[,1], levels = stringr::str_sort(unique(labelsdf[,1]), numeric = T) )
  
  names(labelsdf)[1] <- 'Individual Clusters'
  
  
  
  indi_int_clust_ap <- scDAPP::alluvialplot(labelsdf)
  
  
  return(indi_int_clust_ap)
  
})
names(indi_int_clust_ap_list) <- sample_metadata$Code


summaryplots_integrated <- list(dcond=dcond,
                                d2_int=d2_int,
                                d1_int=d1_int,
                                dcond_split=dcond_split,
                                ap_cond_to_clust=ap_cond_to_clust,
                                ap_samp_to_clust=ap_samp_to_clust,
                                hm_int=hm_int,
                                hm_int_avg=hm_int_avg,
                                int_qc_vln=int_qc_vln,
                                indi_int_clust_ap_list = indi_int_clust_ap_list)




### save pdfs to a subdir of outdir_int
outdir_int_plots <- paste0(outdir_int, '/integration_summaryplots/')

dir.create(outdir_int_plots)

pdf( paste0(outdir_int_plots, '/summaryplots_integrated.pdf'), height = 9, width=9 )

print(summaryplots_integrated)

dev.off()






### cell type plots

if(use_labeltransfer == T){
  
  
  
  #umap of cell type calls (calculated in individual samples)
  d3_int <- DimPlot(sobjint, label = T, repel = T, group.by = 'top_celltype_thresholded')
  
  #umap showing top cell type call score (calculated in individual samples)
  d4_int <- FeaturePlot(SetIdent(sobjint, value = 'top_celltype_thresholded', ), label = T, repel = T, features = 'top_celltype_call_seurat_score')
  
  
  #heatmap of the prediction scores per cluster
  # use scaled values, comparable between clusters
  # get avgs
  avgs <- AverageExpression(sobjint, assays = 'predictions', return.seurat = F)
  #remove max
  avgs <- head( as.data.frame(avgs) , -1)
  # remove empty prediction rows with all 0s
  avgs <- avgs[Matrix::rowSums(avgs)>0,]
  #make column titles nicer
  colnames(avgs) <- gsub('predictions.', 'cluster_', colnames(avgs))
  #format as numeric matrix
  avgs <- as.matrix(avgs)
  ### scale --> this emphasizes diffs between clusters, seems to put related cell types together
  avgs <- t(scale(t(avgs)))
  #select middle value  for color scale
  medval <- mean(avgs)
  #plot it
  hm_ctscores <- ComplexHeatmap::Heatmap(avgs, 
                                         name = 'Scaled\nmean prediction\nscores per cluster', 
                                         column_title = 'Integrated', 
                                         rect_gp = grid::gpar(col = "white", lwd = 0.5), 
                                         circlize::colorRamp2(c(min(avgs), medval, max(avgs)), c("blue", "white", "red")))
  
  
  
  #for dotplot, add dendrogram and row label order
  # it may throw a warning about drawing plot first etc, 
  #shouldn't be an issue if we set seed, which we did
  suppressWarnings(
    dend <- row_dend(hm_ctscores)
  )
  
  #get ordered row labels for dotplot and other plots
  suppressWarnings(
    ct_ordered <- rownames(hm_ctscores@matrix)[ComplexHeatmap::row_order(hm_ctscores)]
  )
  
  
  nicedend <- ggdendro::ggdendrogram(rev(dend), rotate = T) +   
    scale_y_reverse(expand = c(0.05, 0))+
    theme(axis.text.y = element_blank(),
          axis.text.x = element_blank())
  
  dp_ctscores <- DotPlot(sobjint, assay = 'predictions', rev(ct_ordered)) + 
    coord_flip() + 
    theme(axis.title.y=element_blank(),
          axis.text.y =  element_text(hjust = 0),
          axis.text.x = element_text(size=5) )+
    scale_color_gradient2(low = 'blue', high = 'red', mid = 'grey')+
    xlab(label = 'Cluster')+
    guides(color = guide_colorbar(title = "Scaled Average\nPrediction Score"))
  
  
  dp_ctscores <- patchwork::wrap_plots(list(nicedend, dp_ctscores), widths = c(0.3,1))
  
  
  
  #get exp of the celltypes
  rs <- Matrix::rowSums(sobjint@assays$predictions@data)
  rs <- head( rs , -1)
  
  #keep only celltypes that are exp
  rs <- sort(rs[rs>0], decreasing = T)
  ct_in <- names(rs)
  
  
  # heatmap of reference markers
  m_reference <- readRDS(m_reference_path)
  
  #make sure to remove underscores, since seurat doesn't like it in label transfer feature names
  m_ref_small <- m_reference
  
  #as of Nov 9 2023 (Seurat v5); add score
  m_ref_small$score <- (m_ref_small$pct.1 - m_ref_small$pct.2) * m_ref_small$avg_log2FC
  
  
  lt_ref_levs <- levels(m_ref_small$cluster)
  lt_ref_levs <- gsub('_', '-', lt_ref_levs)
  m_ref_small$cluster <- plyr::mapvalues(m_ref_small$cluster, from = levels(m_ref_small$cluster), lt_ref_levs)
  
  m_ref_small <- m_ref_small[m_ref_small$gene %in% rownames(sobjint),]
  m_ref_small <- m_ref_small[m_ref_small$cluster %in% ct_in,]
  n <- 5
  top <- m_ref_small %>% group_by(cluster) %>% top_n(n = n, wt = score)
  
  genes <- top$gene
  
  
  #scale the relevant genes
  sobjint <- ScaleData(sobjint, features = genes, verbose = verbose)
  
  #make sure genes are in 
  # if( any( !(genes %in% rownames(sobjint@assays$SCT@scale.data)) ) ){
  #   missinggenes <- genes[!(genes %in% rownames(sobjint@assays$SCT@scale.data))]
  #   sobjint <- GetResidual(sobjint, genes)
  # }
  
  #prep heatmap
  top <- top[top$gene %in% rownames(sobjint),]
  gem <- sobjint@assays$RISC@scale.data
  gem <- gem[match(top$gene, rownames(gem)),]
  
  #annot for clusters
  #first order gem by cluster...
  md <- sobjint@meta.data
  md <- md[order(md$seurat_clusters),]
  gem <- gem[,match(rownames(md), colnames(gem))]
  
  clust_bc <- setNames(md$seurat_clusters,
                       nm = colnames(gem)
  )
  
  col_clust <- setNames(scales::hue_pal()(length(levels(sobjint$seurat_clusters))),
                        nm = levels(sobjint$seurat_clusters))
  
  ha_clust <- ComplexHeatmap::HeatmapAnnotation(Cluster = clust_bc, col = list(Cluster = col_clust), show_legend = F)
  
  
  
  #annot for markers
  
  #set genes according to ct_ordered
  top$cluster <-factor(top$cluster, levels=ct_ordered)
  top <- top[order(top$cluster),]
  gem <- gem[match(top$gene, rownames(gem)),]
  
  ct_gene <- setNames(top$cluster,
                      nm=top$gene)
  coul <- RColorBrewer::brewer.pal(8, "Set2") 
  coul <- colorRampPalette(coul)(length(unique(top$cluster)))
  col_gene <- setNames(coul, nm = unique(top$cluster))
  
  ha_genes <- ComplexHeatmap::rowAnnotation(Celltype = ct_gene, col = list(Celltype = col_gene),
                                            show_annotation_name=F)
  
  
  #restrict range
  gem[gem>5] <- 5
  gem[gem<-5] <- -5
  
  
  #actual heatmap
  hm_refmarkers <- ComplexHeatmap::Heatmap(gem,
                                           # column_title = 'Integrated clusters',
                                           column_labels = rep('', ncol(gem)),
                                           row_names_gp = grid::gpar(fontsize = 5),
                                           column_split = md$seurat_clusters,
                                           row_split = top$cluster,
                                           row_title_gp = grid::gpar(fontsize = 5),
                                           row_gap = unit(0.8, "mm"), 
                                           column_gap = unit(0.8, "mm"),
                                           row_title_rot = 0,
                                           column_title_rot = 45,
                                           column_title_gp = grid::gpar(fontsize = 7),
                                           name = 'Scaled\nExpression',
                                           cluster_columns = F,
                                           cluster_rows = F,
                                           top_annotation = ha_clust,
                                           left_annotation = ha_genes,
                                           use_raster = F)
  
  
  
  
  #try to average the matrix
  avgl <- lapply(levels(md$seurat_clusters), function(clust){
    mdc <- md[md$seurat_clusters==clust,]
    gemc <- gem[,colnames(gem) %in% rownames(mdc)]
    avg <- matrix(rowMeans(gemc), 
                  dimnames = list(rownames(gem), clust))
    avg
  })
  avg <- do.call('cbind',avgl)
  
  #need to re-prep column annot
  clust_bc <- factor(str_sort(colnames(avg), numeric = T), levels = str_sort(colnames(avg), numeric = T))
  
  col_clust <- setNames(scales::hue_pal()(length(levels(sobjint$seurat_clusters))),
                        nm = levels(sobjint$seurat_clusters))
  
  ha_clust <- ComplexHeatmap::HeatmapAnnotation(Cluster = clust_bc, col = list(Cluster = col_clust))
  
  
  hm_refmarkers_avg <- ComplexHeatmap::Heatmap(avg,
                                               # column_title = 'Integrated clusters',
                                               row_names_gp = grid::gpar(fontsize = 6),
                                               column_names_gp = grid::gpar(fontsize = 6),
                                               column_split = factor(str_sort(colnames(avg), numeric = T), levels = str_sort(colnames(avg), numeric = T)), 
                                               column_title_rot = 0,
                                               column_names_rot = 0, 
                                               
                                               row_split = top$cluster,
                                               row_title_gp = grid::gpar(fontsize = 6),
                                               row_gap = unit(0.5, "mm"), 
                                               column_gap = unit(0.5, "mm"),
                                               row_title_rot = 0,
                                               name = 'Scaled\nExpression',
                                               cluster_columns = F,
                                               cluster_rows = F,
                                               top_annotation = ha_clust,
                                               left_annotation = ha_genes,
                                               use_raster = F)
  
  
  
  
  
  
  #alluvial plot, int clusters to cell type
  labelsdf <- mdint[,c('seurat_clusters', 'top_celltype_thresholded')]
  ap_int_celltypes <- alluvialplot(labelsdf)
  
  
  
  
  
  # cluster-celltype mapping table; still in mem.
  # clustmaxdf
  
  # cluster-celltypr mapped umap
  d5 <- DimPlot(sobjint, group.by = 'INT_celltype_cluster_prediction',
                label = T, repel = T)
  
  
  # vlnplot of score over cluster
  # one plot per condition?
  
  #get celltypes
  ct_ordered <- rownames(hm_ctscores@matrix)[ComplexHeatmap::row_order(hm_ctscores)]
  
  
  #split samples by condition
  
  DefaultAssay(sobjint) <- 'predictions'
  
  
  #make plots of integrated samples
  ctplots_int <- lapply(ct_ordered, function(ct){
    
    
    vct <- VlnPlot(sobjint, ct, pt.size = 0.1)
    
    
    list(vct=vct)
    
    
    
    
  })
  
  names(ctplots_int) <- ct_ordered
  
  DefaultAssay(sobjint) <- 'RISC'
  
  
  
  
  summaryplots_integrated_ct <- list(
    
    hm_ctscores=hm_ctscores,
    dp_ctscores=dp_ctscores,
    
    hm_refmarkers=hm_refmarkers,
    hm_refmarkers_avg=hm_refmarkers_avg,
    
    
    
    d3_int=d3_int,
    d4_int=d4_int,
    ap_int_celltypes=ap_int_celltypes,
    
    clustmaxdf = clustmaxdf,
    d5=d5,
    
    ctplots_int=ctplots_int
    
  )
  
  
  
  
  
  
  pdf( paste0(labeltransfer_outdir_int, '/LabelTransferSummaryPlots.pdf'), height = 9, width=9 )
  
  
  for(i in 1:length(summaryplots_integrated_ct) ){
    
    if(names(summaryplots_integrated_ct)[i] == 'clustmaxdf'){
      print( scDAPP::pdftable(summaryplots_integrated_ct[[i]], title = 'Cluster-Celltype Mapping') )
    } else{
      print(summaryplots_integrated_ct[[i]])
    }
    
  }
  
  dev.off()
  
  
}

8 Integrated cluster summary plots

plotlab <- "


## UMAP colored by condition

Here we plot a UMAP of the integrated dataset colored according to the sample’s condition. Overall we expect to observe a good overlap, while some sections may be quite distinct. This can correspond to clusters of cell states or cell types that are present in one sample but absent from another.


"

cat( plotlab )

8.1 UMAP colored by condition

Here we plot a UMAP of the integrated dataset colored according to the sample’s condition. Overall we expect to observe a good overlap, while some sections may be quite distinct. This can correspond to clusters of cell states or cell types that are present in one sample but absent from another.

print( summaryplots_integrated$dcond )

plotlab <- "


## UMAP split by condition and colored by sample

Here, the integrated UMAP is split by condition, meaning that cells from Condition A are separated from cells from condition B. Additionally, cells are colored according to their sample origin. This allows us to check the overlap of biological replicates from the same condition.


"

cat( plotlab )

8.2 UMAP split by condition and colored by sample

Here, the integrated UMAP is split by condition, meaning that cells from Condition A are separated from cells from condition B. Additionally, cells are colored according to their sample origin. This allows us to check the overlap of biological replicates from the same condition.

print( summaryplots_integrated$d2_int )

plotlab <- "


## UMAP colored by integrated clusters

Here we plot the same UMAP but this time colored by the integrated clusters. These clusters are important to characterize, as these are the groups that we will compare one-by-one between conditions.


"

cat( plotlab )

8.3 UMAP colored by integrated clusters

Here we plot the same UMAP but this time colored by the integrated clusters. These clusters are important to characterize, as these are the groups that we will compare one-by-one between conditions.

print( summaryplots_integrated$d1_int )

plotlab <- "


## UMAP split by condition and colored by integrated clusters

Here we plot the UMAP split by conditon and colored by integrated clusters. This is useful to see compositional differences in which clusters may be enriched or depleted between conditions.


"

cat( plotlab )

8.4 UMAP split by condition and colored by integrated clusters

Here we plot the UMAP split by conditon and colored by integrated clusters. This is useful to see compositional differences in which clusters may be enriched or depleted between conditions.

print( summaryplots_integrated$dcond_split )

plotlab <- "


## Heatmap of integrated cluster markers

Here we plot the top 5 markers per cluster for the integrated clusters.


"

cat( plotlab )

8.5 Heatmap of integrated cluster markers

Here we plot the top 5 markers per cluster for the integrated clusters.

print( summaryplots_integrated$hm_int )

plotlab <- "


## Averaged heatmap of integrated cluster markers

Here we plot the top 5 markers per cluster for the integrated clusters after averaging over each cluster. It is similar to the marker heatmap above, but can help visualize the cluster-wise expression by plotting the cluster averages instead of each individual cell.


"

cat( plotlab )

8.6 Averaged heatmap of integrated cluster markers

Here we plot the top 5 markers per cluster for the integrated clusters after averaging over each cluster. It is similar to the marker heatmap above, but can help visualize the cluster-wise expression by plotting the cluster averages instead of each individual cell.

print( summaryplots_integrated$hm_int_avg )

plotlab <- "


## Violin Plots of quality metrics

Here we plots some quality metrics for each integrated cluster including number of UMIs per cell (nCount_RNA), number of unique genes detected per cell (nFeature_RNA), percent mitochondrial gene expression (percent.mito), and percent hemoglobin gene expression (percent.hemoglobin).


"

cat( plotlab )

8.7 Violin Plots of quality metrics

Here we plots some quality metrics for each integrated cluster including number of UMIs per cell (nCount_RNA), number of unique genes detected per cell (nFeature_RNA), percent mitochondrial gene expression (percent.mito), and percent hemoglobin gene expression (percent.hemoglobin).

print( summaryplots_integrated$int_qc_vln )

plotlab <- "


## Alluvial plot mapping condition to integrated clusters

This alluvial plot allows us to see the contribution of each condition to each cluster. This allows us to visualize compositional differences between conditions clearly.


"

cat( plotlab )

8.8 Alluvial plot mapping condition to integrated clusters

This alluvial plot allows us to see the contribution of each condition to each cluster. This allows us to visualize compositional differences between conditions clearly.

print( summaryplots_integrated$ap_cond_to_clust )

plotlab <- "


## Alluvial plot mapping samples to integrated clusters

This alluvial plot allows us to see the contribution of each sample to each cluster. Combined with the condition-focused alluvial plot above, this allows us to verify compositional patterns across biological replicates.


"

cat( plotlab )

8.9 Alluvial plot mapping samples to integrated clusters

This alluvial plot allows us to see the contribution of each sample to each cluster. Combined with the condition-focused alluvial plot above, this allows us to verify compositional patterns across biological replicates.

print( summaryplots_integrated$ap_samp_to_clust )

plotlab <- "


## Per-sample alluvial plots mapping indiviudal sample clusters to integrated clusters

For each sample, we plot an alluvial plot mapping the individual sample clusters with the integrated clusters.


"

cat( plotlab )

8.10 Per-sample alluvial plots mapping indiviudal sample clusters to integrated clusters

For each sample, we plot an alluvial plot mapping the individual sample clusters with the integrated clusters.

indi_int_clust_ap_list <- summaryplots_integrated$indi_int_clust_ap_list



for(i in c(1:length(indi_int_clust_ap_list)) ){
  
  name=names(indi_int_clust_ap_list)[i]
  
  plotlab <- "

### %s

Individual Clusters (left) mapped to Integrated Clusters (right)
  
"
  
  cat(sprintf(plotlab, name))
  
  print(indi_int_clust_ap_list[[i]])
  
}

8.10.1 Healthy_1

Individual Clusters (left) mapped to Integrated Clusters (right)

8.10.2 Healthy_2

Individual Clusters (left) mapped to Integrated Clusters (right)

8.10.3 Covid_Mild_1

Individual Clusters (left) mapped to Integrated Clusters (right)

8.10.4 Covid_Mild_2

Individual Clusters (left) mapped to Integrated Clusters (right)

8.10.5 Covid_Critical_1

Individual Clusters (left) mapped to Integrated Clusters (right)

8.10.6 Covid_Critical_2

Individual Clusters (left) mapped to Integrated Clusters (right)

rm(indi_int_clust_ap_list)


# print( summaryplots_integrated$indi_int_clust_ap_list )

if(use_labeltransfer == T){
  
  
  
  text <- '

# Integrated label transfer summary

Above, we explored the integrated clusters. Here, we review the cell type scores from label transfer. The goal is to understand which cell types make up each cluster.

Each cell is a mixture of cells. Using label transfer, we can get a score for each cell type in the tissue. If a cell is composed of one cell type, it will have a high score for that cell type only. If a cell is composed of a mix of cell types, it may have a moderately high score for two or more cell types.

As mentioned above, each cell may contain a single cell or a mixture of cells and potentially multiple cell types. Thus, assigning a single label to each cell is difficult. Nevertheless, we use the label transfer scores to detect patterns of cell types in each cluster.

Note that we apply a label transfer score cutoff of 0.3. If the cells are below this, they are marked as NA. These may represent cells missing from the reference.

'
  
  cat(text)
  
}

9 Integrated label transfer summary

Above, we explored the integrated clusters. Here, we review the cell type scores from label transfer. The goal is to understand which cell types make up each cluster.

Each cell is a mixture of cells. Using label transfer, we can get a score for each cell type in the tissue. If a cell is composed of one cell type, it will have a high score for that cell type only. If a cell is composed of a mix of cell types, it may have a moderately high score for two or more cell types.

As mentioned above, each cell may contain a single cell or a mixture of cells and potentially multiple cell types. Thus, assigning a single label to each cell is difficult. Nevertheless, we use the label transfer scores to detect patterns of cell types in each cluster.

Note that we apply a label transfer score cutoff of 0.3. If the cells are below this, they are marked as NA. These may represent cells missing from the reference.

if(use_labeltransfer == T){
  
  
  
  
  plotlab <- "


## Heatmap of cell type label transfer scores with integrated clusters

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters.


"
  
  cat( plotlab )
  
  print(summaryplots_integrated_ct$hm_ctscores)
  
  
  
  plotlab <- "


## Dotplot of label transfer cell type scores with integrated clusters

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters. It is very similar to the heatmap above. However, the dotplot also includes information about how many cells express the cell type score. A big dot indicates many cells in that cluster express it at non-zero level. A big gray dot indicates widespread, low score among cells. A big red dot indicates widespread, high score among cells. A small dot indicates few cells express the score. 


"
  
  cat( plotlab )
  
  print(summaryplots_integrated_ct$dp_ctscores)
  
  
  
  
  
  
  plotlab <- "


## Heatmap of reference cell type markers in integrated data

Here we plot the top 5 markers of cell types as sorted by average log2 fold change, which  are derived from the reference dataset. Since these are the top specific markers of each cell type in the reference, their expression pattern should match clusters with a high label transfer score for each given cell type, for example a cluster with high T cell score should express high T cell markers.


"
  
  cat( plotlab )
  
  print(summaryplots_integrated_ct$hm_refmarkers)
  
  
  plotlab <- "


## Heatmap of reference cell type markers averaged across clusters

Here we plot the top 5 reference cell type markers by average log2 fold change, this time using the average cluster expression. The markers are derived from the reference dataset. Using averages allows us to easily see which clusters highly or lowly express the reference cell type markers.


"
  
  cat( plotlab )
  
  print(summaryplots_integrated_ct$hm_refmarkers_avg)
  
  
  
  
  
  
  
  
  
  
  plotlab <- "


## UMAP of Top Scoring Celltype

The top scoring celltype is plotted on the UMAP.


"
  
  cat( plotlab )
  
  print(summaryplots_integrated_ct$d3_int)
  
  
  plotlab <- "


## UMAP of label transfer prediction score

The label transfer prediction score is plotted on the UMAP. This is a measure of confidence. Low scoring cells may indicate a new cell in the data that was not present in the reference.


"
  
  cat( plotlab )
  
  print(summaryplots_integrated_ct$d4_int)
  
  
  
  plotlab <- "


## Alluvial plot mapping between Louvain clusters to top celltypes

This alluvial plot maps cluster labels to top celltype labels for each cell. This can be useful to identify general trends of celltype score per cluster.




"
  
  cat( plotlab )
  
  print(summaryplots_integrated_ct$ap_int_celltypes)
  
  
  
  
  plotlab <- '


## Table showing cluster-celltype mapping

We apply an ad-hoc method to extend the label transfer from individual cells (default) across entire clusters. We do this by taking the average score of each cell type score in the cluster and choosing the max. This can help simplify the analysis and can be less noisy by sharing information across cells, but may also hide intra-cluster variability, such as particular sub-clusters which may correspond to distinct cell sub-types.

We also apply a thresholding approach such that clusters with a max score of less than 0.3 are considered non-transferrable and marked as NA (not applicable). These may reflect clusters of cells which are not in the reference dataset.
Additionally, clusters with a score between 0.3 - 0.5 are marked with the suffix "_putative" to indicate the somewhat uncertain status. All cluster markers should be carefully inspected and checked for cell type, independnetly of the label transfer results.

'
  
  cat( plotlab )
  
  clustmaxprint <- summaryplots_integrated_ct$clustmaxdf
  clustmaxprint$score <- round(clustmaxprint$score, digits = 5)
  print(knitr::kable(clustmaxprint))
  
  
  
  plotlab <- "


## UMAP of cluster-celltype mapping

As described above, we extend the label transfer prediction from individual cells to whole clusters in order to denoise and simplify the analysis. Here, rather than plotting the individual cell predictions, we plot the cluster-level predictions.



"
  
  cat( plotlab )
  
  
  print(summaryplots_integrated_ct$d5)
  
  
  
  
  
  #CELLTYPE FEATUREPLOTS
  
  plotlab <- "


## Reference cell types plotted for integrated data for all cell types

Here we plot a summary panel to analyze the label transfer scores for each cell type in detail. To help identify which cluster strongly matches the cell type, the scores are plotted as Violin Plots.


"
  
  cat( plotlab )
  
  
  template_int_celltypes <- "


### %s


"
  
  invisible(
    
    for( i in c(1:length(summaryplots_integrated_ct$ctplots_int)) ){
      
      ct = names(summaryplots_integrated_ct$ctplots_int)[i]
      
      cat(sprintf(template_int_celltypes, ct))
      
      thisct_plots <- summaryplots_integrated_ct$ctplots_int[[i]]
      
      print(thisct_plots$vct)
      
      # invisible(lapply(thisct_plots$ctplots_conds, print))
      
      
      
    }
    
  )
  
  
}

9.1 Heatmap of cell type label transfer scores with integrated clusters

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters.

9.2 Dotplot of label transfer cell type scores with integrated clusters

Here we plot the label transfer scores, which are used to predict cell types from the reference single-cell RNAseq dataset. The values are scaled, allowing easy comparison across clusters. It is very similar to the heatmap above. However, the dotplot also includes information about how many cells express the cell type score. A big dot indicates many cells in that cluster express it at non-zero level. A big gray dot indicates widespread, low score among cells. A big red dot indicates widespread, high score among cells. A small dot indicates few cells express the score.

9.3 Heatmap of reference cell type markers in integrated data

Here we plot the top 5 markers of cell types as sorted by average log2 fold change, which are derived from the reference dataset. Since these are the top specific markers of each cell type in the reference, their expression pattern should match clusters with a high label transfer score for each given cell type, for example a cluster with high T cell score should express high T cell markers.

9.4 Heatmap of reference cell type markers averaged across clusters

Here we plot the top 5 reference cell type markers by average log2 fold change, this time using the average cluster expression. The markers are derived from the reference dataset. Using averages allows us to easily see which clusters highly or lowly express the reference cell type markers.

9.5 UMAP of Top Scoring Celltype

The top scoring celltype is plotted on the UMAP.

9.6 UMAP of label transfer prediction score

The label transfer prediction score is plotted on the UMAP. This is a measure of confidence. Low scoring cells may indicate a new cell in the data that was not present in the reference.

9.7 Alluvial plot mapping between Louvain clusters to top celltypes

This alluvial plot maps cluster labels to top celltype labels for each cell. This can be useful to identify general trends of celltype score per cluster.

9.8 Table showing cluster-celltype mapping

We apply an ad-hoc method to extend the label transfer from individual cells (default) across entire clusters. We do this by taking the average score of each cell type score in the cluster and choosing the max. This can help simplify the analysis and can be less noisy by sharing information across cells, but may also hide intra-cluster variability, such as particular sub-clusters which may correspond to distinct cell sub-types.

We also apply a thresholding approach such that clusters with a max score of less than 0.3 are considered non-transferrable and marked as NA (not applicable). These may reflect clusters of cells which are not in the reference dataset. Additionally, clusters with a score between 0.3 - 0.5 are marked with the suffix “_putative” to indicate the somewhat uncertain status. All cluster markers should be carefully inspected and checked for cell type, independnetly of the label transfer results.

cluster	max	score	label
1	NK	0.77652	NK
2	B	1.00000	B
3	T	0.99582	T
4	MNP	0.99599	MNP
5	MNP	1.00000	MNP
6	MNP	0.94171	MNP
7	T	0.78191	T

9.9 UMAP of cluster-celltype mapping

As described above, we extend the label transfer prediction from individual cells to whole clusters in order to denoise and simplify the analysis. Here, rather than plotting the individual cell predictions, we plot the cluster-level predictions.

9.10 Reference cell types plotted for integrated data for all cell types

Here we plot a summary panel to analyze the label transfer scores for each cell type in detail. To help identify which cluster strongly matches the cell type, the scores are plotted as Violin Plots.

9.10.1 MNP

9.10.2 T

9.10.3 NK

9.10.4 B

10 Compositional analysis

Here, we test for differences in the abundance of celltypes (clusters) between conditions. This is referred to as compositional analysis.

If we are comparing two conditions such as treated versus untreated or knockout vs wildtype, one biological effect we can try to test for is the difference in the cell type compositional abundance. The idea that tissue cell type compositions can change in response to biological stimuli has been called “polycreodism” and may reflect particular cell type sensitivity to the perturbation (Lappalainen & Greally 2017). For example, it would likely of interest to note that a knockout or drug completely ablates a certain celltype, reduces another by half, increases another by 25%, and leaves others unchanged. Biologically, these kinds of differences can be explained by various mechanisms, such as differentiation of certain cell types being specifically impaired by a knockout, or specific cell types being particularly sensitive to a drug.

We use the scRNA-seq data to test for differential compositional abundance of each cluster. If replicates are present, we make use of these to test differential abdundance across conditions using the “Propeller” method from the R package “speckle” (Phipson et al 2022). This method calculates cell proportions for each sample, applies a variance stabilizing transformation, and then compares proportion across conditions via linear regression. It is similar to pseudobulk differential expression analysis in that it takes into accounts having multiple biological replicate samples for each condition. Specifically, we use the arcsin square root transformation by setting “transform = asin”. We picked this test and this transformation based on the results of a benchmarking study comparing compositional analysis methods (Simmons 2022). Though Propeller calculates False Discovery Rates (FDR), we define significance using only nominal P value for power reasons.

If replicates are not available, we use an ad-hoc method that relies on the R prop.test() function, which is very similar to the ChiSquare test (see this forum discussion). This works by calculating the proportion of cells in each condition and comparing the proportions. Because there are often thousands of cells being compared, P values can be extremely low. However, this method can result in false-positive conclusions, as the results of 1-vs-1 sample comparisons may not extend to the populations the samples are drawn from. Thus, we advise caution, and interpretation without replicates should be considered as preliminary / pilot data.

compositional_test <- ifelse(Pseudobulk_mode == T, yes = 'propeller', no = 'chisq' )

comp_result <- compositional_analysis_module(sobjint = sobjint,
                                             comps = comps,
                                             sample_metadata = sample_metadata,
                                             outdir_int = outdir_int,
                                             grouping_variable = 'seurat_clusters',
                                             compositional_test = compositional_test)


composition_comps <- comp_result$composition_comps
globalcomposition <- comp_result$globalcomposition

rm(comp_result)

plotlab <- '
## Global cell numbers and proportions

Here we show tables of cell numbers and proportions. These can be used to calculate proportion shifts.
'

cat(plotlab)

10.1 Global cell numbers and proportions

Here we show tables of cell numbers and proportions. These can be used to calculate proportion shifts.

plotlab <- '
### Table of cell numbers

We display a table of cell numbers for each sample and cluster.
'
cat(plotlab)

10.1.1 Table of cell numbers

We display a table of cell numbers for each sample and cluster.

cellstab <- as.data.frame.matrix(globalcomposition$cellstab)
cellstab <- cbind(rownames(cellstab), cellstab)
colnames(cellstab)[1] <- 'Cluster'
knitr::kable(cellstab)

Cluster	Healthy_1	Healthy_2	Covid_Mild_1	Covid_Mild_2	Covid_Critical_1	Covid_Critical_2
1	254	196	236	282	83	201
2	199	187	193	98	191	193
3	132	183	155	102	186	158
4	111	141	148	170	180	162
5	47	17	12	8	0	3
6	7	17	9	6	2	2
7	0	1	3	1	7	25

plotlab <- '
### Table of cell proportions

We display a table of cell proportions for each sample and cluster. This is calculated by dividing each column by the sample total. We then round to the third digit for display purposes (though the full proportion table is saved in outputs).
'
cat(plotlab)

10.1.2 Table of cell proportions

We display a table of cell proportions for each sample and cluster. This is calculated by dividing each column by the sample total. We then round to the third digit for display purposes (though the full proportion table is saved in outputs).

proptab <- as.data.frame.matrix(globalcomposition$proptab)
printproptab <- round(proptab, 3)
printproptab <- cbind(rownames(printproptab), printproptab)
colnames(printproptab)[1] <- 'Cluster'

knitr::kable(printproptab)

Cluster	Healthy_1	Healthy_2	Covid_Mild_1	Covid_Mild_2	Covid_Critical_1	Covid_Critical_2
1	0.339	0.264	0.312	0.423	0.128	0.270
2	0.265	0.252	0.255	0.147	0.294	0.259
3	0.176	0.247	0.205	0.153	0.287	0.212
4	0.148	0.190	0.196	0.255	0.277	0.218
5	0.063	0.023	0.016	0.012	0.000	0.004
6	0.009	0.023	0.012	0.009	0.003	0.003
7	0.000	0.001	0.004	0.001	0.011	0.034

plotlab <- '
### Heatmap of cell proportions in each sample

Here we display a heatmap-style table of cell proportions for each sample and cluster. It is similar to the table above, but we have turned on hierarchical clustering of the rows, to try to visualize patterns of abundance among the clusters.
'
cat(plotlab)

10.1.3 Heatmap of cell proportions in each sample

Here we display a heatmap-style table of cell proportions for each sample and cluster. It is similar to the table above, but we have turned on hierarchical clustering of the rows, to try to visualize patterns of abundance among the clusters.

print(globalcomposition$hmprop)

plotlab <- '
## Differential abundance compositional analysis across conditions

Here we summarize differential abundance across conditions as defined in the cross-condition comparisons provided.
'

cat(plotlab)

10.2 Differential abundance compositional analysis across conditions

Here we summarize differential abundance across conditions as defined in the cross-condition comparisons provided.

if(Pseudobulk_mode == T){
  plotlab <- '
Replicates were provided, so we run the propeller test to take into account abundance in each replicate in the cross-condition comparison.
'
} else{
  plotlab <- '
This analysis was run with the prop.test() function in R to compare overall cell proportions between conditions.
'
}

cat(plotlab)

Replicates were provided, so we run the propeller test to take into account abundance in each replicate in the cross-condition comparison.

compslen <- 1:nrow(comps)
compidx = 1 #for testing
#prep names
comps$labels <- paste0(comps$c1, '_vs_', comps$c2)

for(compidx in 1:length(compslen) ){
  
  
  #get comparison condition levels
  c1 <- comps[compidx,1]
  c2 <- comps[compidx,2]
  
  #get comp lab
  lab <- comps[compidx,3]
  
  #get comp analysis
  diffcomp <- composition_comps[[lab]]
  
  #get testused
  comptestused <- ifelse(T == T,
                         yes = 'propeller', 
                         no = 'R prop.test()')
  
  
  
  plotlab <- '
### %s
'
  cat(sprintf(plotlab, lab))
  
  
  ## print heatmap
  
  plotlab <- '
#### Heatmap of compositional analysis results

Here we plot a heatmap of the cell proportions and compositional analysis from the %s analysis.

Clusters with significant compositional differences are labelled with two asterisks (ie * 1 *). Significance is defined as P < 0.05.

'
  cat(sprintf(plotlab, comptestused))
  print(diffcomp$hmprop_comp)
  
  
  
  
  ## print table
  
  plotlab <- '

#### Table of compositional analysis results

Here we plot a table of the cell proportions and compositional analysis from the %s analysis. We round to the 3rd digit for display, which can result in low P values being shown as zeros (though the full analysis is saved in the output folder).

Clusters with significant compositional differences are labelled with two asterisks (ie * 1 *). Significance is defined as P < 0.05. FDR is not used to define significance but is provided for convenience.

'
  
  cat(sprintf(plotlab, comptestused))
  print_compres <- diffcomp$compres
  print_compres[,-1] <- round(print_compres[,-1], 3)
  print(knitr::kable(print_compres))
  
}

10.2.1 Covid_Critical_vs_Healthy

10.2.1.1 Heatmap of compositional analysis results

Here we plot a heatmap of the cell proportions and compositional analysis from the propeller analysis.

Clusters with significant compositional differences are labelled with two asterisks (ie * 1 *). Significance is defined as P < 0.05.

10.2.1.2 Table of compositional analysis results

Here we plot a table of the cell proportions and compositional analysis from the propeller analysis. We round to the 3rd digit for display, which can result in low P values being shown as zeros (though the full analysis is saved in the output folder).

Clusters with significant compositional differences are labelled with two asterisks (ie * 1 *). Significance is defined as P < 0.05. FDR is not used to define significance but is provided for convenience.

	BaselineProp.clusters	BaselineProp.Freq	PropMean.Covid_Critical	PropMean.Healthy	PropRatio	Tstatistic	P.Value	FDR
7	* 7 *	0.011	0.022	0.001	32.936	2.231	0.043	0.104
4	4	0.206	0.248	0.169	1.465	1.721	0.107	0.188
3	3	0.228	0.249	0.211	1.181	0.809	0.432	0.504
2	2	0.267	0.277	0.259	1.070	0.361	0.723	0.723
1	* 1 *	0.254	0.199	0.301	0.660	-2.204	0.045	0.104
6	6	0.010	0.003	0.016	0.179	-1.253	0.231	0.323
5	* 5 *	0.023	0.002	0.043	0.047	-3.027	0.009	0.063

10.2.2 Covid_Mild_vs_Healthy

10.2.2.1 Heatmap of compositional analysis results

Here we plot a heatmap of the cell proportions and compositional analysis from the propeller analysis.

Clusters with significant compositional differences are labelled with two asterisks (ie * 1 *). Significance is defined as P < 0.05.

10.2.2.2 Table of compositional analysis results

Here we plot a table of the cell proportions and compositional analysis from the propeller analysis. We round to the 3rd digit for display, which can result in low P values being shown as zeros (though the full analysis is saved in the output folder).

Clusters with significant compositional differences are labelled with two asterisks (ie * 1 *). Significance is defined as P < 0.05. FDR is not used to define significance but is provided for convenience.

	BaselineProp.clusters	BaselineProp.Freq	PropMean.Covid_Mild	PropMean.Healthy	PropRatio	Tstatistic	P.Value	FDR
7	7	0.002	0.003	0.001	4.057	0.630	0.539	0.629
4	4	0.196	0.225	0.169	1.333	1.371	0.192	0.345
1	1	0.332	0.367	0.301	1.219	1.353	0.197	0.345
3	3	0.196	0.179	0.211	0.847	-0.784	0.446	0.625
2	2	0.232	0.201	0.259	0.777	-1.394	0.185	0.345
6	6	0.013	0.010	0.016	0.648	-0.430	0.674	0.674
5	5	0.029	0.014	0.043	0.326	-1.635	0.124	0.345

10.2.3 Covid_Critical_vs_Covid_Mild

10.2.3.1 Heatmap of compositional analysis results

Here we plot a heatmap of the cell proportions and compositional analysis from the propeller analysis.

Clusters with significant compositional differences are labelled with two asterisks (ie * 1 *). Significance is defined as P < 0.05.

10.2.3.2 Table of compositional analysis results

Here we plot a table of the cell proportions and compositional analysis from the propeller analysis. We round to the 3rd digit for display, which can result in low P values being shown as zeros (though the full analysis is saved in the output folder).

Clusters with significant compositional differences are labelled with two asterisks (ie * 1 *). Significance is defined as P < 0.05. FDR is not used to define significance but is provided for convenience.

	BaselineProp.clusters	BaselineProp.Freq	PropMean.Covid_Critical	PropMean.Covid_Mild	PropRatio	Tstatistic	P.Value	FDR
7	7	0.013	0.022	0.003	8.119	2.168	0.089	0.208
3	3	0.213	0.249	0.179	1.394	1.752	0.147	0.243
2	2	0.240	0.277	0.201	1.377	1.602	0.177	0.243
4	4	0.234	0.248	0.225	1.099	0.565	0.599	0.599
1	1	0.285	0.199	0.367	0.542	-2.460	0.063	0.208
6	6	0.007	0.003	0.010	0.276	-1.471	0.208	0.243
5	5	0.008	0.002	0.014	0.145	-2.195	0.086	0.208

### clean up env
rm(print_compres, diffcomp, composition_comps, globalcomposition)
invisible(gc(full = T, reset = F, verbose = F))

11 Differential expression across conditions

Below we summarize the results of cross-condition differential expression (DE) analysis, for example KO vs WT, or disease vs healthy, or drugged vs control. As in single-cell data, we stratify the cross-condition DE by cluster. In other words, using the clusters defined above, we compare KO vs WT in cluster 1, cluster 2, and so on. For these types of analysis, it is recommended to have multiple biological replicates per condition.

If multiple replicates are available, then the best way to take advantage of these is to use a “pseudobulk” strategy. This refers to an approach in which cells from each sample are pooled together, and bulk RNA-seq analysis methods are used to identify DE genes across conditions. Pseudobulk differential expression analysis is done with EdgeR likelihood ratio test, as recommended by Squair et al 2021 Nat Com.

If replicates are not available, it is still possible to compare conditions. This is done using a wilcoxon test approach. Drawing conclusions from the results of this type of analysis are inherently limited as they constitute “N of 1” experiments, but can be considered as preliminary/pilot data.

if(Pseudobulk_mode == T){
  
  detestused_text <- 'The differential expression analysis here used a pseudobulk edgeR approach. First, cells are "pseudobulked", or combined,  at the cluster level by adding up the gene UMI counts in all cells in each cluster for each replicate. Next, EdgeR with the likelihood ratio test is applied.'
  
}
if(Pseudobulk_mode == F){
  
  detestused_text <- 'The differential expression analysis here used a non-pseudobulk Wilcoxon test approach. The normalized counts of each gene are compared between cells in condition A vs B in each cluster.'
  
}


cat(detestused_text)

The differential expression analysis here used a pseudobulk edgeR approach. First, cells are “pseudobulked”, or combined, at the cluster level by adding up the gene UMI counts in all cells in each cluster for each replicate. Next, EdgeR with the likelihood ratio test is applied.

#prep names
comps$labels <- paste0(comps$c1, '_vs_', comps$c2)

#read sobjlist back in? keep it in?
# will need to optimize memory

#get cluster object name
clustname <- risc_clust_lab


if(Pseudobulk_mode == T){
  
  m_bycluster_crosscondition_de_comps <- de_across_conditions_module(
    sobjint = sobjint,
    sample_metadata = sample_metadata,
    comps = comps,
    grouping_variable = clustname,
    outdir_int=outdir_int,
    assay = 'RNA', slot = 'counts',
    Pseudobulk_mode = T,
    cluster_prefix = T,
    crossconditionDE_padj_thres = crossconditionDE_padj_thres,
    crossconditionDE_lfc_thres = crossconditionDE_lfc_thres,
    crossconditionDE_min.pct = 0.1
  )
  
} else{
  
  m_bycluster_crosscondition_de_comps <- de_across_conditions_module(
    sobjint = sobjint,
    sample_metadata = sample_metadata,
    comps = comps,
    grouping_variable = clustname,
    outdir_int=outdir_int,
    assay = 'RISC', slot = 'data',
    Pseudobulk_mode = F,
    cluster_prefix = T,
    crossconditionDE_padj_thres = crossconditionDE_padj_thres,
    crossconditionDE_lfc_thres = crossconditionDE_lfc_thres,
    crossconditionDE_min.pct = 0
  )
  
}

compslen <- 1:nrow(comps)
compidx = 1 #for testing


outdir_DE <- paste0(outdir_int, '/differentialexpression_crosscondition/')
outdir_DE_plots <- paste0(outdir_DE, '/plots/')
dir.create(outdir_DE_plots, recursive = T)


DEplots_comps <- lapply(compslen, function(compidx){
  
  
  #get comparison condition levels
  c1 <- comps[compidx,1]
  c2 <- comps[compidx,2]
  
  #get comp lab
  lab <- comps[compidx,3]
  
  message(lab)
  
  #get cross conditions res per cluster list
  m_bycluster_crosscondition_de <- m_bycluster_crosscondition_de_comps[[compidx]]
  
  
  #get num DEGs
  crossconditionDE_min.pct <- ifelse(Pseudobulk_mode == T, yes = 0.1, no = 0)
  numdegs <- sapply(m_bycluster_crosscondition_de, function(m){
    
    #normal fdr and padj thresholds
    m <- m[m$FDR < crossconditionDE_padj_thres,, drop=F]
    m <- m[abs(m$logFC) > crossconditionDE_lfc_thres,, drop=F]
    
    #pct thresholds: +FC, pct1 > 0.1; -FC, pct2 > 0.1
    
    upm <- m[m$logFC > 0,,drop=F]
    upm <- upm[upm$pct.1 > crossconditionDE_min.pct,, drop=F]
    
    dnm <- m[m$logFC < 0,,drop=F]
    dnm <- dnm[dnm$pct.2 > crossconditionDE_min.pct,, drop=F]
    
    m <- rbind(upm,dnm)
    
    try( table( factor(sign(m$logFC), levels=c(-1,1)) ) )
  })
  
  numdegs <- t(numdegs)
  colnames(numdegs) <- c(c2, c1)
  
  #make sure all clusters are shown
  # make a fake df and replace fake with real res
  saved_cluster_levels <- paste0('cluster_',levels(sobjint$seurat_clusters))
  numdegs_all <- data.frame(Cluster = saved_cluster_levels,
                            c1 = 0, c2 = 0)
  colnames(numdegs_all) <- c('Cluster', c1, c2)
  rownames(numdegs_all) <- numdegs_all$Cluster
  
  numdegs_all[rownames(numdegs), c1] <- numdegs[,c1]
  numdegs_all[rownames(numdegs), c2] <- numdegs[,c2]
  rownames(numdegs_all) <- NULL
  
  
  
  ### at least one DEG must be present ###
  numdegs <- numdegs[ (numdegs[,1] > 0) | (numdegs[,2] > 0) , ] 
  
  
  #make sure all samples from pmd are in the mlist...
  subpmd <- sample_metadata[sample_metadata$Condition %in% c(c1,c2),]
  
  #for each cluster, get the up/dn DEGs
  i = 3
  clustres <- lapply(1:nrow(numdegs), function(i){
    
    clustlab <- rownames(numdegs)[i]
    m <- m_bycluster_crosscondition_de[[clustlab]]
    
    m <- m[m$FDR < crossconditionDE_padj_thres,, drop=F]
    m <- m[abs(m$logFC) > crossconditionDE_lfc_thres,, drop=F]
    
    crossconditionDE_min.pct <- ifelse(Pseudobulk_mode == T, yes = 0.1, no = 0)
    
    upm <- m[m$logFC > 0,,drop=F]
    upm <- upm[upm$pct.1 > crossconditionDE_min.pct,, drop=F]
    
    dnm <- m[m$logFC < 0,,drop=F]
    dnm <- dnm[dnm$pct.2 > crossconditionDE_min.pct,, drop=F]
    
    m <- rbind(upm,dnm)
    
    
    #make sure all samples are in top
    # only if de pseudobulk
    if(Pseudobulk_mode == T){
      
      if( any(!(subpmd$Code %in% colnames(m))) ){
        missingcodes <- subpmd$Code[!(subpmd$Code %in% colnames(m))]
        missingcodecols <- lapply(missingcodes, function(code){
          missingdf <- data.frame(missing = rep(0, nrow(m)) )
          colnames(missingdf) <- code
          return(missingdf)
        })
        
        missingcodecols <- dplyr::bind_cols(missingcodecols)
        
        
        
        #bind missing cols to results
        m <- cbind(m,missingcodecols)
        
      }
      
      #make sure order of samples is consistent
      m_nosamp <- m[,!(colnames(m) %in% subpmd$Code)]
      msamp <- m[,colnames(m) %in% subpmd$Code,drop=F]
      msamp <- msamp[, match(subpmd$Code, colnames(msamp)) ]
      
      m <- cbind(m_nosamp, msamp)
      
      
      
    }
    
    
    m$cluster <- clustlab
    
    return(m)
    
  })
  
  names(clustres) <- rownames(numdegs)
  
  # clustres <- do.call(rbind, clustres)
  
  
  
  
  
  
  
  
  
  
  
  # ### summary heatmap ###
  # 
  # # first make a summary heatmap, top 3 DEGs from each cluster up/dn...
  # 
  # top <- lapply(clustres, function(m){
  #   top <- head( m[m$logFC > 0,,drop=F] , n = 3 ) 
  #   btm <- tail( m[m$logFC < 0,,drop=F] , n = 3 )
  #   
  #   subm <- rbind(top,btm)
  #   
  #   return(subm)
  # })
  # 
  # top <- do.call(rbind, top)
  # rownames( top ) <- NULL
  # 
  # 
  # #to make things easier, remove "cluster_"
  # top$cluster <- gsub('cluster_', '', top$cluster)
  # 
  # 
  # 
  # genes <- top$gene_symbol
  # 
  # 
  # #subset for just these conditions
  # md <- sobjint@meta.data
  # md <- md[md$Condition %in% c(c1,c2),]
  # sobjsub <- sobjint[,rownames(md)]
  # 
  # #scale the relevant genes
  # # sobjsub <- ScaleData(sobjsub, features = genes, verbose = verbose)
  # 
  # 
  # # get the gene matrix
  # # gem <- sobjsub@assays$RISC@scale.data
  # gem <- sobjsub@assays$RISC@data
  # 
  # 
  # #prep heatmap
  # top <- top[top$gene %in% rownames(sobjsub),]
  # gem <- gem[match(genes, rownames(gem)),]
  # 
  # #as matrix
  # gem <- as.matrix(gem)
  # 
  # 
  # #annot for clusters
  # 
  # 
  # #first order gem by cluster...
  # md <- sobjsub@meta.data
  # md <- md[order(md$seurat_clusters),]
  # 
  # #within cluster, order by condition...
  # mdx <- lapply( unique(md$seurat_clusters) , function(clust){
  #   mdsub <- md[md$seurat_clusters==clust,]
  #   
  #   mdc1 <- mdsub[mdsub$Condition == c1,,drop=F]
  #   mdc2 <- mdsub[mdsub$Condition == c2,,drop=F]
  #   
  #   mdsub <- rbind(mdc1,mdc2)
  #   return(mdsub)
  #   
  # })
  # md <- do.call(rbind, mdx)
  # 
  # #match gem order too
  # gem <- gem[,match(rownames(md), colnames(gem))]
  # 
  # #cluster annot and colors
  # clust_bc <- setNames(md$seurat_clusters,
  #                      nm = colnames(gem)
  # )
  # col_clust <- setNames(scales::hue_pal()(length(levels(sobjsub$seurat_clusters))),
  #                       nm = levels(sobjsub$seurat_clusters))
  # 
  # #condition annot and colors
  # cond_bc <- setNames(md$Condition,
  #                     nm = colnames(gem))
  # 
  # #for cond colors, set up color scheme...
  # set2 <- c("#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3")
  # 
  # 
  # set2 <- set2[1:length(unique(sample_metadata$Condition))]
  # 
  # col_cond <- setNames(set2,
  #                      nm = unique(sample_metadata$Condition) )
  # 
  # col_cond <- col_cond[names(col_cond) %in% c(c1,c2)]
  # 
  # 
  # 
  # ha_clust <- ComplexHeatmap::HeatmapAnnotation(Cluster = clust_bc, 
  #                                               Condition = cond_bc,
  #                                               
  #                                               col = list(Cluster = col_clust,
  #                                                          Condition = col_cond),
  #                                               show_legend = F)
  # 
  # 
  # 
  # #annot for markers
  # #set genes according to ct_ordered
  # top$cluster <- factor(top$cluster, levels=unique(top$cluster))
  # 
  # #condition; can just use +/- lfc
  # top$Condition <- c1
  # top[top$logFC < 0, 'Condition'] <- c2
  # 
  # 
  # #match order of genes of gem with stats res
  # gem <- gem[match(top$gene, rownames(gem)),]
  # 
  # 
  # #set up annot for cluster for genes
  # ct_gene <- setNames(top$cluster,
  #                     nm=top$gene_symbol)
  # col_gene <- col_clust
  # # col_gene <- col_gene[names(col_gene) %in% top$cluster]
  # 
  # #set up annot for condition for genes
  # cond_gene <- setNames(top$Condition,
  #                       nm = top$gene_symbol)
  # col_genecond <- col_cond
  # 
  # 
  # ha_genes <- ComplexHeatmap::rowAnnotation(Cluster = ct_gene,
  #                                           Condition = cond_gene,
  #                                           col = list(Cluster = col_gene, Condition = col_genecond),
  #                                           show_annotation_name=F)
  # 
  # 
  # #restrict range
  # # gem[gem>5] <- 5
  # # gem[gem<-5] <- -5
  # gem <- log1p(gem)
  # 
  # pal <- circlize::colorRamp2(c(0,  max(gem)), c("white", "red"))
  # 
  # #actual heatmap
  # hm_DE <- ComplexHeatmap::Heatmap(gem,
  #                                  # column_title = 'Integrated clusters',
  #                                  column_labels = rep('', ncol(gem)),
  #                                  row_names_gp = grid::gpar(fontsize = 5),
  #                                  column_split = md$seurat_clusters,
  #                                  row_split = top$cluster,
  #                                  row_title_gp = grid::gpar(fontsize = 5),
  #                                  row_gap = unit(0.8, "mm"), 
  #                                  column_gap = unit(0.8, "mm"),
  #                                  border_gp = gpar(col = "black", lwd = 0.1),
  #                                  row_title_rot = 0,
  #                                  column_title_rot = 45,
  #                                  column_title_gp = grid::gpar(fontsize = 7), 
  #                                  col = pal,
  #                                  name = 'Log1p\nRISC Norm\nCounts',
  #                                  cluster_columns = F,
  #                                  cluster_rows = F,
  #                                  top_annotation = ha_clust,
  #                                  left_annotation = ha_genes,
  #                                  use_raster = F)
  # 
  # 
  # 
  # 
  # 
  # 
  # 
  # 
  # 
  # ### same thing but avg
  # # ie, avg clust 1 c1, avg clust 1 c2, avg clust 2 c1, avg clust 2 c2, etc...
  # 
  # 
  # md <- sobjsub@meta.data
  # md$condclust <- paste0( md$seurat_clusters, '_c1')
  # md[md$Condition == c2,'condclust'] <- paste0( md[md$Condition == c2,'seurat_clusters'], '_c2')
  # oldlevs <- str_sort(unique(md$condclust), numeric = T)
  # levs <- oldlevs
  # levs <- gsub('c1', c1, levs)
  # levs <- gsub('c2', c2, levs)
  # md$condclust <- factor(md$condclust, levels = oldlevs)
  # md$condclust <- plyr::mapvalues(md$condclust, oldlevs, levs)
  # 
  # 
  # sobjsub$condclust <- md$condclust
  # avg <- AverageExpression(sobjsub, assays = 'RISC', slot = 'data', 
  #                          features = rownames(gem),
  #                          group.by = 'condclust')
  # 
  # 
  # avg <- as.matrix(avg[[1]])
  # 
  # ## set up avg heatmap, annots etc ##
  # 
  # #column annots
  # #cluster annot and colors
  # AVGclust_bc <- setNames(str_split_fixed(colnames(avg), '_', 2)[,1],
  #                         nm = colnames(avg)
  # )
  # AVGcol_clust <- setNames(scales::hue_pal()(length(levels(sobjsub$seurat_clusters))),
  #                          nm = levels(sobjsub$seurat_clusters))
  # 
  # #condition annot and colors
  # AVGcond_bc <- setNames(str_split_fixed(colnames(avg), '_', 2)[,2],
  #                        nm = colnames(avg))
  # 
  # #for cond colors, set up color scheme...
  # set2 <- c("#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3")
  # 
  # set2 <- set2[1:length(unique(sample_metadata$Condition))]
  # 
  # col_cond <- setNames(set2,
  #                      nm = unique(sample_metadata$Condition) )
  # 
  # col_cond <- col_cond[names(col_cond) %in% c(c1,c2)]
  # 
  # 
  # 
  # ha_clust <- ComplexHeatmap::HeatmapAnnotation(Cluster = AVGclust_bc, 
  #                                               Condition = AVGcond_bc,
  #                                               
  #                                               col = list(Cluster = AVGcol_clust,
  #                                                          Condition = col_cond),
  #                                               show_legend = F)
  # 
  # 
  # 
  # 
  # #annot for markers
  # #set genes according to ct_ordered
  # # top$cluster <- factor(top$cluster, levels=unique(top$cluster))
  # 
  # #condition; can just use +/- lfc
  # # top$Condition <- c1
  # # top[top$logFC < 0, 'Condition'] <- c2
  # 
  # 
  # #match order of genes of gem with stats res
  # avg <- avg[match(top$gene, rownames(avg)),]
  # 
  # 
  # # #set up annot for cluster for genes
  # # ct_gene <- setNames(top$cluster,
  # #                     nm=top$gene_symbol)
  # # col_gene <- col_clust
  # # # col_gene <- col_gene[names(col_gene) %in% top$cluster]
  # # 
  # # #set up annot for condition for genes
  # # cond_gene <- setNames(top$Condition,
  # #                       nm = top$gene_symbol)
  # # col_genecond <- col_cond
  # # 
  # # 
  # # ha_genes <- ComplexHeatmap::rowAnnotation(Cluster = ct_gene,
  # #                                           Condition = cond_gene,
  # #                                           col = list(Cluster = col_gene, Condition = col_genecond),
  # #                                           show_annotation_name=F)
  # 
  # 
  # #restrict range
  # # gem[gem>5] <- 5
  # # gem[gem<-5] <- -5
  # 
  # avg <- log1p(avg)
  # 
  # pal <- circlize::colorRamp2(c(0,  max(avg)), c("white", "red"))
  # 
  # #make column order not alphabetic...
  # colsplit <- colnames(avg)
  # colsplit <- as.character(colsplit)
  # colsplit <- str_split_fixed(colsplit, '_', 2)[,1]
  # colsplit <- factor( colsplit,
  #                     levels = str_sort(unique(colsplit), numeric = T))
  # 
  # #actual heatmap
  # hm_DE_AVG <- ComplexHeatmap::Heatmap(avg,
  #                                      # column_title = 'Integrated clusters',
  #                                      # column_labels = rep('', ncol(gem)),
  #                                      column_names_gp = grid::gpar(fontsize = 5),
  #                                      column_names_rot = 45,
  #                                      row_names_gp = grid::gpar(fontsize = 5),
  #                                      column_split = colsplit,
  #                                      row_split = top$cluster,
  #                                      row_title_gp = grid::gpar(fontsize = 5),
  #                                      row_gap = unit(0.8, "mm"), 
  #                                      column_gap = unit(0.8, "mm"),
  #                                      border_gp = gpar(col = "black", lwd = 0.1),
  #                                      row_title_rot = 0,
  #                                      column_title_rot = 45,
  #                                      column_title_gp = grid::gpar(fontsize = 7), 
  #                                      col = pal,
  #                                      name = 'Log1p\nRISC Norm\nCounts',
  #                                      cluster_columns = F,
  #                                      cluster_rows = F,
  #                                      top_annotation = ha_clust,
  #                                      left_annotation = ha_genes,
  #                                      use_raster = F)
  # 
  # 
  # 
  # 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  ### for each cluster, make a heatmap of the DEGs using single-cell data
  i=3
  cluster_de_heatmap_l <- lapply(1:length(clustres), function(i){
    
    
    #get DEGs
    m <- clustres[[i]]
    
    #sort degs
    m <- m[order(m$logFC, decreasing = T),]
    
    #get cluster name
    cluster <- names(clustres)[i]
    
    message(cluster)
    
    #get cluster without "cluster_" prefix
    cluster_noprefix <- gsub('cluster_', '', cluster)
    
    #get cluster cells; order by c1 then c2
    md <- sobjint@meta.data
    mdclust <- md[md$seurat_clusters == cluster_noprefix,]
    mdclust <- mdclust[mdclust$Condition %in% c(c1,c2),]
    mdclust$Condition <- factor(mdclust$Condition, levels = c(c1,c2))
    mdclust <- mdclust[order(mdclust$Condition), ]
    
    clustcells <- rownames(mdclust)
    
    #get matrix of DEGs of cells from this cluster
    mat <- sobjint@assays$RISC@data
    mat <- mat[match(m$gene_symbol, rownames(mat)),clustcells,drop=F]
    
    
    #scale matrix
    mat <- as.matrix(mat)
    mat <- t(scale(t(mat)))
    
    #restrict range
    mat[mat > 5 ] <- 5
    mat[mat < -5] <- -5
    
    hm_de_clust <- Heatmap(mat, 
                           show_column_names = F, 
                           cluster_column_slices = F, cluster_columns = F,
                           cluster_row_slices = F, cluster_rows = F,
                           show_row_names = ifelse(nrow(mat)<=50, yes = T, no = F), 
                           name = paste0('Scaled\nRISC\nValues'), 
                           column_split = mdclust$Condition, 
                           row_split = factor(sign(m$logFC), levels = c(1,-1)),
                           border_gp = gpar(col = "black", lwd = 1)
                           
    )
    
    pdf(NULL)
    hm_de_clust <- invisible(
      draw(hm_de_clust, 
           column_title = paste0(lab, '\n', str_to_title(cluster) ),
           row_title = 'Log Fold Change Sign')
    )
    dev.off()
    
    
    
    if(Pseudobulk_mode == T){
      ### just plot the values
      pb_mat <- m[,colnames(m) %in% mdclust$Code]
      
      
      
      pb_mat <- as.matrix(pb_mat)
      pb_mat <- log1p(pb_mat)
      pb_mat <- t(scale(t(pb_mat)))
      
      #restrict range
      pb_mat[pb_mat > 5 ] <- 5
      pb_mat[pb_mat < -5] <- -5
      
      
      #order md properly
      #get just this comp samples
      subpmd <- sample_metadata[sample_metadata$Condition %in% c(c1,c2),]
      
      
      
      #order columns of heatmap by c1 vs c2
      code_comps_order <- subpmd[subpmd$Condition == c1,"Code"]
      code_comps_order <- c(code_comps_order, subpmd[subpmd$Condition == c2,"Code"])
      
      #make sure they are in the matrix
      code_comps_order <- code_comps_order[code_comps_order %in% colnames(pb_mat)]
      
      #also prepare factor for ordering of heatmap
      condition_vector_ordering <- factor(subpmd[match(code_comps_order, subpmd$Code), "Condition"], levels = c(c1,c2))
      
      
      pb_mat <- pb_mat[,code_comps_order,drop=F]
      
      
      hm_de_clust_pb <- Heatmap(pb_mat, 
                                show_column_names = T, 
                                cluster_column_slices = F, cluster_columns = F,
                                cluster_row_slices = F, cluster_rows = F,
                                show_row_names = ifelse(nrow(mat)<=50, yes = T, no = F), 
                                name = paste0('Scaled\nPseudobulk\nValues'),
                                column_split = condition_vector_ordering, 
                                row_split = factor(sign(m$logFC), levels = c(1,-1)),
                                border_gp = gpar(col = "black", lwd = 1)
      )
      
      
      pdf(NULL)
      hm_de_clust_pb <- invisible(
        draw(hm_de_clust_pb, 
             column_title = paste0(lab, '\n', str_to_title(cluster) ),
             row_title = 'Log Fold Change Sign')
      )
      dev.off()
      
      
    }
    
    if(Pseudobulk_mode == F){
      
      #get DEGs
      m <- clustres[[i]]
      
      #sort degs
      m <- m[order(m$logFC, decreasing = T),]
      
      #get cluster name
      cluster <- names(clustres)[i]
      
      #get cluster without "cluster_" prefix
      cluster_noprefix <- gsub('cluster_', '', cluster)
      
      #get cluster cells; order by c1 then c2
      md <- sobjint@meta.data
      mdclust <- md[md$seurat_clusters == cluster_noprefix,]
      mdclust$Condition <- factor(mdclust$Condition, levels = c(c1,c2))
      mdclust <- mdclust[order(mdclust$Condition), ]
      
      clustcells <- rownames(mdclust)
      
      #get matrix of DEGs of cells from this cluster
      mat <- sobjint@assays$RISC@data
      mat <- mat[match(m$gene_symbol, rownames(mat)),clustcells,drop=F]
      
      #order md properly
      #get just this comp samples
      subpmd <- sample_metadata[sample_metadata$Condition %in% c(c1,c2),]
      
      #order columns of heatmap by c1 vs c2
      code_comps_order <- subpmd[subpmd$Condition == c1,"Code"]
      code_comps_order <- c(code_comps_order, subpmd[subpmd$Condition == c2,"Code"])
      
      #make sure sample is even present in cluster
      code_comps_order <- code_comps_order[code_comps_order %in% mdclust$Code]
      
      #also prepare factor for ordering of heatmap
      condition_vector_ordering <- factor(subpmd[match(code_comps_order, subpmd$Code), "Condition"], levels = c(c1,c2))
      
      
      avg_l <- lapply(code_comps_order, function(code){
        codecells <- rownames(mdclust[mdclust$Code == code,])
        avgmat <- Matrix::rowMeans(mat[,codecells, drop=F])
        
        avgmat <- matrix(avgmat)
        rownames(avgmat) <- rownames(mat); colnames(avgmat) <- code
        return(avgmat)
      })
      avgmat <- as.matrix(dplyr::bind_cols(avg_l))
      rownames(avgmat) <- rownames(mat)
      colnames(avgmat) <- code_comps_order
      
      avgmat <- as.matrix(avgmat)
      avgmat <- t(scale(t(avgmat)))
      
      #restrict range
      avgmat[avgmat > 5 ] <- 5
      avgmat[avgmat < -5] <- -5
      
      hm_de_clust_pb <- Heatmap(avgmat, 
                                show_column_names = T, 
                                cluster_column_slices = F, cluster_columns = F,
                                cluster_row_slices = F, cluster_rows = F,
                                show_row_names = ifelse(nrow(mat)<=50, yes = T, no = F), 
                                name = paste0('Scaled\nMean\nRisc\nValues'),
                                column_split = condition_vector_ordering, 
                                row_split = factor(sign(m$logFC), levels = c(1,-1)),
                                border_gp = gpar(col = "black", lwd = 1)
      )
      
      
      pdf(NULL)
      hm_de_clust_pb <- invisible(
        draw(hm_de_clust_pb, 
             column_title = paste0(lab, '\n', str_to_title(cluster) ),
             row_title = 'Log Fold Change Sign')
      )
      dev.off()
      
      
      
      
    }
    
    
    return(list(hm_de_clust = hm_de_clust,
                hm_de_clust_pb = hm_de_clust_pb))
    
  })
  
  
  
  names(cluster_de_heatmap_l) <- names(clustres)
  
  
  deplotlist <- list(#hm_DE = hm_DE,
    #hm_DE_AVG = hm_DE_AVG,
    cluster_de_heatmap_l = cluster_de_heatmap_l)
  
  
  
  
  deplotfile <- paste0(outdir_DE_plots, lab, '.pdf')
  
  pdf(deplotfile, height = 9, width = 9)
  
  # print(deplotlist$hm_DE)
  # print(deplotlist$hm_DE_AVG)
  invisible(print(deplotlist$cluster_de_heatmap_l))
  
  dev.off()
  
  return(deplotlist)
  
  
  
})




names(DEplots_comps) <- comps$labels

### save cluster levels ###
saved_cluster_levels <- levels(sobjint$seurat_clusters)

pwayoutdir <- paste0(outdir_int, '/pathwayanalysis_crosscondition/')
dir.create(pwayoutdir, recursive = T)

#at this point, we no longer need many of the high memory using objects
rm(sobjint, 
   ap_cond_to_clust, ap_int_celltypes, ap_samp_to_clust, avg, avgl, ctplots_int,
   cutoffplots, cutoffs,d1_int, d2_int, d3_int,dcond,dcond_split,dend,dp_ctscores,
   gem,ha_clust,ha_genes,hm_ctscores,hm_int, hm_refmarkers, hm_refmarkers_avg,
   intmd, labelsdf, m_integrated_clusters,m_ref_small, m_reference, md,mdint,nicedend,
   sampsumplots, summaryplots_integrated, summaryplots_integrated_ct, thisct_plots, top,
   d4_int, ip)

invisible(gc(full = T, reset = F, verbose = F))


#prep pathways
pathways <- preppathways_pathwayanalysis_crosscondition_module(species = species,
                                                               outdir_int = outdir_int)

### run main pathway analysis ###
pways_output_list <- pathwayanalysis_crosscondition_module(
  m_bycluster_crosscondition_de_comps = m_bycluster_crosscondition_de_comps,
  pathways = pathways,
  sample_metadata = sample_metadata,
  # deg.weight = "pval", #this is deprecated now
  comps = comps,
  workernum = workernum,
  outdir_int = outdir_int
)


pathway_analysis_mainlist_comps <- pways_output_list$pathway_analysis_mainlist_comps
pathwaysummplots_comps <- pways_output_list$pathwaysummplots_comps



### for easily reproducing plots and etc, save them as R objects...

DE_pathways_plot_objects_list <- list(comps = comps,
                                      m_bycluster_crosscondition_de_comps = m_bycluster_crosscondition_de_comps,
                                      pathway_analysis_mainlist_comps = pathway_analysis_mainlist_comps,
                                      pathwaysummplots_comps = pathwaysummplots_comps,
                                      crossconditionDE_padj_thres = crossconditionDE_padj_thres,
                                      crossconditionDE_lfc_thres = crossconditionDE_lfc_thres)




DE_pathways_plot_objects_list_file <- paste0(pwayoutdir, '/DE_pathways_plot_objects_list.rds')

saveRDS(DE_pathways_plot_objects_list, DE_pathways_plot_objects_list_file)

#### print out NUMDEGS, NUMDEGS THRESHOLDED, AND PWAY ANALYSIS TO HTML


#do this for each comparison


compslen <- nrow(comps)

for(compidx in c(1:compslen) ){
  
  
  #get comparison condition levels
  c1 <- comps[compidx,1]
  c2 <- comps[compidx,2]
  
  #get comp lab
  lab <- comps[compidx,3]
  
  #get cross conditions res per cluster list
  m_bycluster_crosscondition_de <- m_bycluster_crosscondition_de_comps[[compidx]]
  
  #get pway results full list
  pathway_analysis_mainlist <- pathway_analysis_mainlist_comps[[compidx]]
  
  #get pway summary plots
  summplots_cats <- pathwaysummplots_comps[[compidx]]
  
  
  
  # get the deplots
  deplotlist <- DEplots_comps[[lab]]
  
  
  complab <- "


## %s


"
  
  cat(sprintf(complab, lab))
  
  
  
  
  
  if(Pseudobulk_mode == T){
    
    plotlab <- "


### Number of significant DEGs across conditions in each cluster

Here we check number of differentially expressed genes (DEGs) after applying some statistical thresholds:
    
* Adjusted P value < %s

* Log Fold Change > +/- %s

* Proportion of cells expressing gene in condition A > 0.1 if LFC is positive

* Proportion of cells expressing gene in condition B > 0.1 if LFC is negative

Differential expression compares genes in condition A (left) versus condition B (right). The left column indicates the number of genes upregulated in the left condition, while the right column indicates genes upregulated in the right condition.

Finally, the thresholds above do not affect downstream results from pathway analysis, they are just meant to count the number of DEGs. Downstream usage of DEGs can use these thresholds, or you can choose other appropriate cutoffs.

"
    
    cat( sprintf(plotlab, crossconditionDE_padj_thres, crossconditionDE_lfc_thres) )
    
    
    numdegs <- sapply(m_bycluster_crosscondition_de, function(m){
      
      #normal fdr and padj thresholds
      m <- m[m$FDR < crossconditionDE_padj_thres,, drop=F]
      m <- m[abs(m$logFC) > crossconditionDE_lfc_thres,, drop=F]
      
      #pct thresholds: +FC, pct1 > 0.1; -FC, pct2 > 0.1
      
      upm <- m[m$logFC > 0,,drop=F]
      upm <- upm[upm$pct.1 > 0.1,, drop=F]
      
      dnm <- m[m$logFC < 0,,drop=F]
      dnm <- dnm[dnm$pct.2 > 0.1,, drop=F]
      
      m <- rbind(upm,dnm)
      
      try( table( factor(sign(m$logFC), levels=c(-1,1)) ) )
    })
    
    numdegs <- t(numdegs)
    colnames(numdegs) <- c(c2, c1)
    
    #make sure all clusters are shown
    # make a fake df and replace fake with real res
    saved_cluster_levels_withlab <- paste0('cluster_', saved_cluster_levels)
    numdegs_all <- data.frame(Cluster = saved_cluster_levels_withlab,
                              c1 = 0, c2 = 0)
    colnames(numdegs_all) <- c('Cluster', c1, c2)
    rownames(numdegs_all) <- numdegs_all$Cluster
    
    numdegs_all[rownames(numdegs), c1] <- numdegs[,c1]
    numdegs_all[rownames(numdegs), c2] <- numdegs[,c2]
    rownames(numdegs_all) <- NULL
    
    #rename colnames to have high
    colnames(numdegs_all) <- c('Cluster', paste0(c1, '_high'), paste0(c2, '_high'))
    
    print(knitr::kable(numdegs_all))
    
  }
  
  if(Pseudobulk_mode == F){
    
    
    plotlab <- "


### Number of significant DEGs across conditions in each cluster

Here we check number of differentially expressed genes (DEGs) after applying some statistical thresholds:
    
* Adjusted P value < %s

* Log Fold Change > +/- %s

Differential expression compares genes in condition A (left) versus condition B (right). The left column indicates the number of genes upregulated in the left condition, while the right column indicates genes upregulated in the right condition.

Finally, the thresholds above do not affect downstream results from pathway analysis, they are just meant to count the number of DEGs. Downstream usage of DEGs can use these thresholds, or you can choose other appropriate cutoffs.

"
    
    cat( sprintf(plotlab, crossconditionDE_padj_thres, crossconditionDE_lfc_thres) )
    
    
    
    numdegs <- sapply(m_bycluster_crosscondition_de, function(m){
      
      #normal fdr and padj thresholds
      m <- m[m$FDR < crossconditionDE_padj_thres,, drop=F]
      m <- m[abs(m$logFC) > crossconditionDE_lfc_thres,, drop=F]
      
      #pct thresholds for pb_edgeR only: +FC, pct1 > 0.1; -FC, pct2 > 0.1
      
      upm <- m[m$logFC > 0,,drop=F]
      
      dnm <- m[m$logFC < 0,,drop=F]
      
      m <- rbind(upm,dnm)
      
      try( table( factor(sign(m$logFC), levels=c(-1,1)) ) )
    })
    
    numdegs <- t(numdegs)
    colnames(numdegs) <- c(c2, c1)
    
    #make sure all clusters are shown
    # make a fake df and replace fake with real res
    saved_cluster_levels_withlab <- paste0('cluster_', saved_cluster_levels)
    numdegs_all <- data.frame(Cluster = saved_cluster_levels_withlab,
                              c1 = 0, c2 = 0)
    colnames(numdegs_all) <- c('Cluster', c1, c2)
    rownames(numdegs_all) <- numdegs_all$Cluster
    
    numdegs_all[rownames(numdegs), c1] <- numdegs[,c1]
    numdegs_all[rownames(numdegs), c2] <- numdegs[,c2]
    rownames(numdegs_all) <- NULL
    
    #rename colnames to have high
    colnames(numdegs_all) <- c('Cluster', paste0(c1, '_high'), paste0(c2, '_high'))
    
    print(knitr::kable(numdegs_all))
    
    
    
    
  }
  
  
  
  
  
  #   plotlab <- '
  # ### Heatmap of top cross-condition DEGs for each cluster
  # 
  # Here we plot the top 3 differentially expressed genes across conditions from each cluster.
  # In other words, in cluster 1, we have the top 3 DEGs from %s and the top 3 DEGs from %s, then the same in cluster 2, so on.
  # 
  # 
  # 
  # '
  #   
  #   cat(sprintf(plotlab, c1, c2))
  #   
  #   print(deplotlist$hm_DE)
  
  
  #   ### this is kind of bugged, it prints the plot without making the title a heading...
  #   cat('\n\n\n')
  #   
  #   plotlab <- '
  # ### Heatmap of top cross-condition DEGs for after averaging each cluster
  # 
  # Here we plot the top 3 differentially expressed genes across conditions from each cluster.
  # This heatmap is similar to the one above, but rather than showing all the cells from each cluster, we show the cluster averages.
  # In other words, in cluster 1, we have the top 3 DEGs from %s and the top 3 DEGs from %s, then the same in cluster 2, so on.
  # 
  # 
  # 
  # '
  #   
  #   cat(sprintf(plotlab, c1, c2))
  #   
  #   print(deplotlist$hm_DE_AVG)
  
  
  
  
  
  
  
  cat('\n\n\n')
  
  plotlab <- '
### Per-cluster heatmaps of all DEGs

To ensure the differential expression results are robust, it is helpful to inspect all DEGs by visualizing them in a heatmap. For each cluster, we compare the DEGs at both the single cell level, and either the pseudobulk level (if we used pseudobulk_edgeR) or the average RISC value level (if using wilcox).



'
  
  cat(sprintf(plotlab))
  
  cluster_de_heatmap_l <- deplotlist$cluster_de_heatmap_l
  
  
  for(i in 1:length(cluster_de_heatmap_l) ){
    clust = names(cluster_de_heatmap_l)[i]
    clust = str_to_title(clust)
    
    clustplots <- cluster_de_heatmap_l[[i]]
    
    
    plotlab <- '
#### %s


'
    
    cat(sprintf(plotlab, clust))
    
    
    plotlab <- '
##### Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.


'
    
    cat(plotlab)
    
    
    
    print(clustplots[[1]])
    
    
    cat('\n\n\n')
    
    
    if(Pseudobulk_mode == T){
      plotlab <- '
##### Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.


'
    }
    
    
    if(Pseudobulk_mode == F){
      plotlab <- '
##### Differentially Expressed Gene Heatmap plotted after averaging RISC values for samples for this cluster

Here, we plot all significant DEGs after averaging the RISC-transformed values for samples in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.


'
    }
    
    
    
    
    cat(plotlab)
    
    
    print(clustplots[[2]])
    
    
    
    cat('\n\n\n')
    
    
  }
  
  rm(cluster_de_heatmap_l, clustplots)
  
  
  
  
  
  
  
  
  
  pwayDElab <- "


### Gene set enrichment analysis across conditions

Pathway analysis for the cross-condition analysis is performed for overexpressed and underexpressed genes for each cluster. This is done via Gene Set Enrichment Analysis (GSEA) [(Subramanian et al 2005)](https://www.pnas.org/doi/10.1073/pnas.0506580102).

GSEA is preferred over other pathway analysis such as fisher tests or chi-square tests because it does not require making arbitrary cutoffs to the number of DEGs and takes into account how strongly differentially expressed each gene may be. For the latter, data-driven gene-specific weight is applied. We use a standard weighting method of -log10(P-value) * sign of Log Fold Change.

The pathways we choose in pathway analysis are derived from the Molecular Signatures Database (MSIGDB) where they are sorted by categories, such as Gene Ontology (GO) Biological Process, GO Molecular Function, KEGG, Reactome, etc. These are databases that annotate genes by function or molecular pathway.


"
  
  cat(pwayDElab)
  
  
  
  
  
  
  # print the pathway analysis
  
  
  pathway_analysis_main_nonull <- pathway_analysis_mainlist[lengths(pathway_analysis_mainlist) != 0]
  
  
  #loop through each category:
  # loop thru each cluster
  # get up/down pathways if there are any in each cluster
  # make some adjustments to the table to adjust for printing: shorten gene list
  # print for whole category:
  # 1. summary of whole category, up pathways 
  # 2. summary of whole category, dn pathways
  # 3. cluster by cluster, up tables, up dotplots
  # 4. cluster by cluster, dn tables, dn dotplots
  
  for(i in 1:length(pathway_analysis_main_nonull) ){
    
    #get category name
    cat <- names(pathway_analysis_main_nonull)[i]
    
    
    #get actual cluster results, and remove NAs...
    pwaycat <- pathway_analysis_main_nonull[[i]]
    
    
    # remove clusters with no pathways --> this sets them to list of lengths 0
    pwaycat = lapply(pwaycat, function(clust){clust[lengths(clust) != 0]})
    
    # remove the clusters with no pathways by removing the lists of length 0
    pwaycat <- pwaycat[lengths(pwaycat) != 0]
    
    
    ### if the category has NO PATHWAYS SIGNIFICANT in ANY cluster, just skip it
    if(length(pwaycat) == 0){
      next
    }
    
    
    
    #in each category, loop thru each cluster
    
    #loop through each cluster's results; this list contains up/down for each pathway
    # j is the cluster index
    #for(j in 1:length(pwaycat) ){
    clustlist <- lapply( c( 1:length(pwaycat) ), function(j){
      
      
      #get cluster name
      clust <- names(pwaycat)[j]
      
      
      
      #get this clusters up/down list, and remove if null
      clustcat <- pwaycat[[j]]
      clustcat <- clustcat[lengths(clustcat) != 0]
      
      if(length(clustcat)==0){
        return()
      }
      
      
      
      #get the results for this cluster
      dirreslist <- clustcat
      
      #get the plot
      dp <- dirreslist$dp
      
      #get the table
      gseares <- dirreslist$gseares
      
      
      
      
      
      # use the dotplot data to subset only significant
      gseares_fromplot <- dp$data
      
      #fix up so we can match the two
      gseares_fromplot$Description <- gseares_fromplot$pathway
      gseares_fromplot$Description <- gsub(' ', replacement = '_', gseares_fromplot$Description)
      gseares_fromplot$Description <- gsub('\n', replacement = '_', gseares_fromplot$Description)
      
      gseares <- gseares[match(gseares_fromplot$Description, gseares$pathway),]
      
      
      #modify cpres... keep only important columns...
      cpshow <- gseares
      rownames(cpshow) <- NULL
      
      cpshow <- cpshow[,c('pathway', "NES", "ES", "pval", "padj", "log2err", "size", "leadingEdge")]
      
      #keep only top 5 leading edge genes
      top5gene <- sapply(cpshow$leadingEdge, function(x){
        x <- x[x!='']
        
        if(length(x) > 5){
          x <- head(x,5)
          x[6] <- '...'
        }
        
        paste(x, collapse = '/')
      })
      
      
      cpshow$leadingEdge <- top5gene
      
      
      #instead of printing, output a list of them
      
      return(
        list(cpshow=cpshow,
             dp=dp)
      )
      
      
      ## return just the cpshow
      #return(cpshow)
      
      
      
      
    }) #close cluster lapply
    
    
    names(clustlist) <- names(pwaycat)
    
    
    
    #get the summaryplots
    # i is the category index
    summplots_conds <- summplots_cats[[i]]
    
    
    
    
    
    
    
    # print for whole category:
    # 1. summary dotplot of whole category, up pathways 
    # 2. summary dotplot of whole category, dn pathways
    # 3. cluster by cluster, up/dn table and dotplots
    
    #print category label
    catlab <- "


#### %s


"
    
    #prep cluster label, will print this for each cluster
    cat(sprintf(catlab, cat))
    
    
    
    
    
    for(condidx in 1:length(c(c1,c2)) ){
      
      cond <- c(c1,c2)[condidx]
      
      
      summplot <- summplots_conds[[condidx]]
      
      
      
      
      if( is.null(summplot) ){
        
        summlab <- "


##### Summary %s, no pathways enriched
    
This category of pathways had no signifcantly enriched pathways in %s


"
        
        cat(sprintf(summlab, cond, cond))
        
        next
        
      }
      
      
      
      summlab <- "


##### Summaryplot: %s
    
Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in %s. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.


"
      
      cat(sprintf(summlab, cond, cond))
      
      print(summplot)
      
      
      
      
      
    }
    
    
    
    
    
    
    clustlab <- "


##### Per-cluster pathway results
      
Here we plot the pathways that are significantly enriched in the differentially expressed genes between %s and %s for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in %s relative to %s, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.



"
    
    cat(sprintf(clustlab, c1, c2, c1, c2))
    
    
    #for each cluster, print out the dotplot and table,
    # or a print a message saying none significant
    for(clust in names(clustlist) ){
      
      
      
      
      
      clust_cond_lab <- "





###### %s


"
      
      cat(sprintf(clust_cond_lab, clust))
      
      
      
      clust_plot_tab <- clustlist[[clust]]
      
      
      
      cpshow <- clust_plot_tab$cpshow
      dp <- clust_plot_tab$dp
      
      
      
      print( knitr::kable(cpshow) )
      print(dp)
      
      
      
      
      
    } #close cluster printing loop
    
    
  } # close category by category for loop
  
  
  
} # close comparisons loop

11.1 Covid_Critical_vs_Healthy

11.1.1 Number of significant DEGs across conditions in each cluster

Here we check number of differentially expressed genes (DEGs) after applying some statistical thresholds:

Adjusted P value < 0.1
Log Fold Change > +/- 0
Proportion of cells expressing gene in condition A > 0.1 if LFC is positive
Proportion of cells expressing gene in condition B > 0.1 if LFC is negative

Differential expression compares genes in condition A (left) versus condition B (right). The left column indicates the number of genes upregulated in the left condition, while the right column indicates genes upregulated in the right condition.

Finally, the thresholds above do not affect downstream results from pathway analysis, they are just meant to count the number of DEGs. Downstream usage of DEGs can use these thresholds, or you can choose other appropriate cutoffs.

Cluster	Covid_Critical_high	Healthy_high
cluster_1	306	133
cluster_2	63	16
cluster_3	69	26
cluster_4	389	192
cluster_5	0	0
cluster_6	36	41
cluster_7	0	0

11.1.2 Per-cluster heatmaps of all DEGs

To ensure the differential expression results are robust, it is helpful to inspect all DEGs by visualizing them in a heatmap. For each cluster, we compare the DEGs at both the single cell level, and either the pseudobulk level (if we used pseudobulk_edgeR) or the average RISC value level (if using wilcox).

11.1.2.1 Cluster_1

11.1.2.1.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.1.2.1.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.1.2.2 Cluster_2

11.1.2.2.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.1.2.2.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.1.2.3 Cluster_3

11.1.2.3.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.1.2.3.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.1.2.4 Cluster_4

11.1.2.4.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.1.2.4.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.1.2.5 Cluster_6

11.1.2.5.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.1.2.5.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.1.3 Gene set enrichment analysis across conditions

Pathway analysis for the cross-condition analysis is performed for overexpressed and underexpressed genes for each cluster. This is done via Gene Set Enrichment Analysis (GSEA) (Subramanian et al 2005).

GSEA is preferred over other pathway analysis such as fisher tests or chi-square tests because it does not require making arbitrary cutoffs to the number of DEGs and takes into account how strongly differentially expressed each gene may be. For the latter, data-driven gene-specific weight is applied. We use a standard weighting method of -log10(P-value) * sign of Log Fold Change.

The pathways we choose in pathway analysis are derived from the Molecular Signatures Database (MSIGDB) where they are sorted by categories, such as Gene Ontology (GO) Biological Process, GO Molecular Function, KEGG, Reactome, etc. These are databases that annotate genes by function or molecular pathway.

11.1.3.1 HALLMARK

11.1.3.1.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.1.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.1.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.1.3.1.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_E2F_TARGETS	2.728753	0.8201163	0.0000000	0.0000000	1.5565442	192	TK1/STMN1/MKI67/CDC20/BIRC5/…
HALLMARK_G2M_CHECKPOINT	2.637151	0.7966623	0.0000000	0.0000000	1.3877461	172	STMN1/MKI67/UBE2C/CDC20/BIRC5/…
HALLMARK_MYC_TARGETS_V1	2.090822	0.6294245	0.0000000	0.0000000	0.8140358	190	TYMS/CDC20/MCM4/DUT/PCNA/…
HALLMARK_MITOTIC_SPINDLE	2.013879	0.6113177	0.0000000	0.0000004	0.7195128	165	BIRC5/TOP2A/CENPF/TPX2/PLK1/…
HALLMARK_ESTROGEN_RESPONSE_LATE	1.950394	0.6695374	0.0000248	0.0002477	0.5756103	70	AREG/PRSS23/CISH/ZFP36/FABP5/…
HALLMARK_MTORC1_SIGNALING	1.666299	0.5043537	0.0002052	0.0017100	0.5188481	180	MCM4/RRM2/PLK1/DHFR/MCM2/…
HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION	1.783066	0.6326189	0.0002650	0.0018928	0.4984931	60	AREG/LGALS1/TNFAIP3/JUN/TIMP1/…
HALLMARK_GLYCOLYSIS	1.645698	0.5158091	0.0008169	0.0051056	0.4772708	129	STMN1/CDK1/HMMR/NASP/AURKA/…
HALLMARK_SPERMATOGENESIS	1.611055	0.5784368	0.0039929	0.0174638	0.4070179	54	CDKN3/CCNB2/CDK1/NCAPH/KIF2C/…
HALLMARK_APOPTOSIS	1.589836	0.4989284	0.0041913	0.0174638	0.4070179	127	TOP2A/LGALS3/HMGB2/PMAIP1/BAX/…

11.1.3.1.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_TNFA_SIGNALING_VIA_NFKB	2.322689	0.6313845	0.0000000	0.0000000	0.8986712	140	KLF2/RHOB/CD69/JUN/ZBTB10/…
HALLMARK_INFLAMMATORY_RESPONSE	1.936725	0.5591517	0.0000070	0.0001753	0.6105269	95	CD69/EMP3/KLF6/MYC/NFKBIA/…
HALLMARK_P53_PATHWAY	1.738750	0.4725668	0.0000771	0.0012851	0.5384341	143	JUN/BAX/PPP1R15A/TOB1/FOS/…
HALLMARK_APOPTOSIS	1.742682	0.4860557	0.0002342	0.0029277	0.5188481	123	RHOB/CD69/JUN/PMAIP1/IRF1/…
HALLMARK_IL6_JAK_STAT3_SIGNALING	1.827284	0.5843341	0.0008299	0.0082987	0.4772708	49	JUN/STAT1/IRF1/SOCS1/IL10RB/…
HALLMARK_IL2_STAT5_SIGNALING	1.600515	0.4456460	0.0013672	0.0097654	0.4550599	124	RHOB/KLF6/MYC/PLIN2/IFITM3/…
HALLMARK_ESTROGEN_RESPONSE_EARLY	1.624859	0.4931969	0.0051265	0.0284806	0.4070179	67	MYC/AREG/TOB1/FOS/LRIG1/…
HALLMARK_HYPOXIA	1.464556	0.4187076	0.0141024	0.0705121	0.3807304	100	JUN/KLF6/PLIN2/PPP1R15A/FOS/…
HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION	1.564335	0.4953251	0.0173514	0.0722976	0.3524879	52	RHOB/JUN/EMP3/AREG/TGFBI/…
HALLMARK_ESTROGEN_RESPONSE_LATE	1.471464	0.4399010	0.0168521	0.0722976	0.3524879	73	AREG/FOS/SGK1/NXT1/LARGE1/…
HALLMARK_FATTY_ACID_METABOLISM	-1.397312	-0.3773208	0.0240373	0.0924511	0.3524879	102	ACO2/NSDHL/ECI2/RETSAT/HSP90AA1/…
HALLMARK_OXIDATIVE_PHOSPHORYLATION	-1.507742	-0.3757902	0.0009974	0.0083119	0.4550599	190	NDUFS3/ACO2/MTX2/CASP7/TOMM70/…

11.1.3.1.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_TNFA_SIGNALING_VIA_NFKB	2.162940	0.6192789	0.0000000	0.0000000	0.8012156	130	CD69/SGK1/SOCS3/NFKBIA/IFNGR2/…
HALLMARK_IL2_STAT5_SIGNALING	2.039593	0.5811499	0.0000000	0.0000007	0.7337620	136	PIM1/PTGER2/TNFRSF4/TNFRSF18/SOCS2/…
HALLMARK_INFLAMMATORY_RESPONSE	2.055382	0.6083283	0.0000002	0.0000032	0.6901325	103	CD69/PTGER2/NFKBIA/IFNGR2/OSM/…
HALLMARK_IL6_JAK_STAT3_SIGNALING	2.085060	0.6931223	0.0000017	0.0000208	0.6435518	50	PIM1/SOCS3/IFNGR2/JUN/TNFRSF1A/…
HALLMARK_HYPOXIA	1.952689	0.5823359	0.0000025	0.0000252	0.6272567	99	PIM1/FOS/JUN/ZFP36/LDHA/…
HALLMARK_UV_RESPONSE_UP	1.814924	0.5412512	0.0000541	0.0004512	0.5573322	99	NFKBIA/FOS/SIGMAR1/POLG2/JUNB/…
HALLMARK_ALLOGRAFT_REJECTION	1.738773	0.4936802	0.0000857	0.0006122	0.5384341	139	EIF5A/IFNGR2/IL16/CD40LG/SOCS1/…
HALLMARK_INTERFERON_GAMMA_RESPONSE	1.697457	0.4750014	0.0001210	0.0007562	0.5384341	159	PIM1/CD69/ARID5B/SOCS3/NFKBIA/…
HALLMARK_ESTROGEN_RESPONSE_LATE	1.797445	0.5662766	0.0006332	0.0035175	0.4772708	69	SGK1/AREG/FOS/ZFP36/NXT1/…
HALLMARK_TGF_BETA_SIGNALING	1.630331	0.5733072	0.0053376	0.0242618	0.4070179	38	IFNGR2/ID3/JUNB/UBE2D3/SMURF2/…

11.1.3.1.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_COMPLEMENT	2.170216	0.6672156	0.0000000	0.0000006	0.7477397	137	CLU/PIM1/S100A12/S100A9/CTSD/…
HALLMARK_INTERFERON_GAMMA_RESPONSE	1.993342	0.5952694	0.0000004	0.0000090	0.6749629	173	PIM1/FCGR1A/HLA-DRB1/NFKBIA/CXCL10/…
HALLMARK_APOPTOSIS	1.929190	0.6000240	0.0000246	0.0004097	0.5756103	125	CLU/GNA15/TXNIP/GADD45B/EMP1/…
HALLMARK_TNFA_SIGNALING_VIA_NFKB	1.837970	0.5585377	0.0001026	0.0012823	0.5384341	150	MAP3K8/BCL2A1/NFKBIA/CXCL10/MARCKS/…
HALLMARK_INFLAMMATORY_RESPONSE	1.844172	0.5735815	0.0001305	0.0013048	0.5188481	125	ADM/CD82/NFKBIA/CXCL10/GNA15/…
HALLMARK_CHOLESTEROL_HOMEOSTASIS	1.851895	0.6407820	0.0003801	0.0027152	0.4984931	61	CLU/FABP5/FADS2/FDPS/S100A11/…
HALLMARK_IL2_STAT5_SIGNALING	1.776333	0.5500254	0.0003491	0.0027152	0.4984931	130	PIM1/MAP3K8/CCND3/SLC39A8/CXCL10/…
HALLMARK_HYPOXIA	1.779982	0.5631392	0.0007326	0.0045790	0.4772708	109	PIM1/ADM/LDHA/SLC2A3/TGFBI/…
HALLMARK_COAGULATION	1.785994	0.6332655	0.0010480	0.0055257	0.4550599	53	CLU/GNB2/CTSB/SERPING1/MAFF/…
HALLMARK_MYOGENESIS	1.803834	0.5986199	0.0016842	0.0076553	0.4550599	78	CLU/TNNT1/GADD45B/MEF2C/IGFBP7/…

11.1.3.1.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_ESTROGEN_RESPONSE_LATE	1.798427	0.5196117	0.0047554	0.0792569	0.4070179	48	AREG/S100A9/TSPAN13/ISG20/ATP2B4/…
HALLMARK_INTERFERON_GAMMA_RESPONSE	1.557464	0.3934974	0.0034506	0.0792569	0.4317077	128	IFI27/IFITM3/HLA-DQA1/FCGR1A/HLA-DRB1/…
HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION	1.798903	0.5420057	0.0074444	0.0930552	0.4070179	36	AREG/VCAN/JUN/WIPF1/BASP1
HALLMARK_OXIDATIVE_PHOSPHORYLATION	-1.360880	-0.4361828	0.0041952	0.0792569	0.4070179	180	TCIRG1/ACO2/UQCRFS1/ATP6V1G1/NDUFA4/…

11.1.3.2 GO_BP

11.1.3.2.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.2.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.2.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.1.3.2.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_MITOTIC_CELL_CYCLE_PROCESS	2.189977	0.6119397	0.0000000	0.0000000	1.3188888	560	STMN1/MKI67/UBE2C/CDC20/MCM4/…
GOBP_MITOTIC_NUCLEAR_DIVISION	2.265175	0.6666883	0.0000000	0.0000000	1.0476265	239	MKI67/UBE2C/CDC20/CENPF/TPX2/…
GOBP_ORGANELLE_FISSION	2.164888	0.6197788	0.0000000	0.0000000	1.0476265	346	MKI67/UBE2C/CDC20/TOP2A/CENPF/…
GOBP_SISTER_CHROMATID_SEGREGATION	2.270348	0.6893656	0.0000000	0.0000000	0.9325952	167	UBE2C/CDC20/TOP2A/CENPF/PLK1/…
GOBP_MITOTIC_SISTER_CHROMATID_SEGREGATION	2.229011	0.6878440	0.0000000	0.0000000	0.8634154	147	UBE2C/CDC20/CENPF/PLK1/PTTG1/…
GOBP_PROTEIN_DNA_COMPLEX_ASSEMBLY	2.183707	0.6743030	0.0000000	0.0000000	0.8390889	148	H3C2/CENPF/ASF1B/CENPN/CDC45/…
GOBP_MITOTIC_SPINDLE_ORGANIZATION	2.195368	0.6955760	0.0000000	0.0000005	0.7881868	109	STMN1/CDC20/TPX2/PLK1/KIF23/…
GOBP_REGULATION_OF_CHROMOSOME_SEGREGATION	2.226644	0.7419976	0.0000000	0.0000043	0.7477397	73	MKI67/UBE2C/CENPF/PLK1/ZWINT/…
GOBP_METAPHASE_ANAPHASE_TRANSITION_OF_CELL_CYCLE	2.157218	0.7545796	0.0000002	0.0000364	0.6901325	55	UBE2C/CENPF/PLK1/ZWINT/MAD2L1/…
GOBP_REGULATION_OF_CHROMOSOME_SEPARATION	2.158176	0.7428544	0.0000002	0.0000501	0.6749629	61	UBE2C/CENPF/PLK1/PTTG1/ZWINT/…
GOBP_PROCESS_UTILIZING_AUTOPHAGIC_MECHANISM	-1.284567	-0.2997422	0.0020314	0.0825689	0.4317077	408	WDR45B/C9orf72/ERCC4/SIRT1/PSEN1/…
GOBP_POSITIVE_REGULATION_OF_PROTEIN_EXIT_FROM_ENDOPLASMIC_RETICULUM	-1.788954	-0.8818964	0.0018853	0.0783333	0.4550599	7	TMEM30A/BCAP31/EDEM1
GOBP_NEGATIVE_REGULATION_OF_ACTIN_NUCLEATION	-1.789769	-0.9239111	0.0007261	0.0370781	0.4772708	6	HIP1R/CORO1A/GMFB

11.1.3.2.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_ADAPTIVE_IMMUNE_RESPONSE	1.941814	0.4773758	0.0000000	0.0000007	0.8390889	375	IGLV7-46/IGLV3-9/IGHV3-72/IGHV3-49/IGKV1D-17/…
GOBP_POSITIVE_REGULATION_OF_B_CELL_ACTIVATION	2.108893	0.5992717	0.0000000	0.0001095	0.7337620	107	IGHV3-72/IGHV3-49/IGHV6-1/PELI1/IGHV7-81/…
GOBP_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN	2.155604	0.6492334	0.0000001	0.0002414	0.7049757	75	IGHV3-72/IGHV3-49/IGHV6-1/IGHV7-81/IGHV2-70/…
GOBP_B_CELL_RECEPTOR_SIGNALING_PATHWAY	2.021007	0.5799128	0.0000006	0.0009960	0.6594444	101	IGHV3-72/IGHV3-49/IGHV6-1/BAX/IGHV7-81/…
GOBP_B_CELL_ACTIVATION	1.844998	0.4695816	0.0000007	0.0009960	0.6594444	244	IGHV3-72/IGHV3-49/KLF6/IGHV6-1/PELI1/…
GOBP_COMPLEMENT_ACTIVATION	2.107365	0.6394987	0.0000011	0.0011963	0.6435518	70	IGHV3-72/IGHV3-49/IGHV6-1/IGHV7-81/IGHV2-70/…
GOBP_PHAGOCYTOSIS_RECOGNITION	2.057115	0.6349051	0.0000050	0.0033870	0.6105269	65	IGHV3-72/IGHV3-49/IGHV6-1/IGHV7-81/IGHV2-70/…
GOBP_MEMBRANE_INVAGINATION	1.968101	0.5630140	0.0000050	0.0033870	0.6105269	102	IGHV3-72/IGHV3-49/IGHV6-1/IGHV7-81/IGHV2-70/…
GOBP_CELL_RECOGNITION	1.966266	0.5547062	0.0000041	0.0033870	0.6105269	112	IGHV3-72/IGHV3-49/IGHV6-1/IGHV7-81/IGHV2-70/…
GOBP_GLIAL_CELL_PROLIFERATION	1.963143	0.7576787	0.0002560	0.0796927	0.4984931	21	MYC/AREG/TSPO/PPP1CC/PLAG1/…
GOBP_REGULATION_OF_CARTILAGE_DEVELOPMENT	-1.903577	-0.7564689	0.0002825	0.0821368	0.4984931	17	SMPD3/TRPS1/SMAD3/SOX5/GLG1/…
GOBP_CYTOPLASMIC_TRANSLATION	-1.796263	-0.4698042	0.0000408	0.0203316	0.5573322	135	RPL41/RPS15A/RPS16/RPL37A/RPL29/…

11.1.3.2.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_RESPONSE_TO_ORGANIC_CYCLIC_COMPOUND	1.892491	0.4842527	0.0000000	0.0000014	0.8140358	367	PIM1/SGK1/PTGER2/AREG/ZFP36L1/…
GOBP_RECEPTOR_SIGNALING_PATHWAY_VIA_STAT	2.033450	0.6192620	0.0000006	0.0003507	0.6594444	84	SOCS3/TNFRSF18/SOCS2/OSM/TNFRSF1A/…
GOBP_RESPONSE_TO_CORTICOSTEROID	2.075931	0.6506483	0.0000015	0.0005411	0.6435518	67	SGK1/AREG/ZFP36L1/FOS/ZFP36/…
GOBP_TYROSINE_PHOSPHORYLATION_OF_STAT_PROTEIN	2.018249	0.7167395	0.0000220	0.0053147	0.5756103	34	SOCS3/TNFRSF18/OSM/TNFRSF1A/SOCS1/…
GOBP_RESPONSE_TO_CAMP	1.988106	0.7060350	0.0000484	0.0097861	0.5573322	34	AREG/ZFP36L1/FDX1/ITPR2/RAP1A/…
GOBP_RESPONSE_TO_PURINE_CONTAINING_COMPOUND	1.952358	0.6489001	0.0000749	0.0137962	0.5384341	48	AREG/ZFP36L1/FOS/TRPM2/FDX1/…
GOBP_STEM_CELL_PROLIFERATION	1.910631	0.7318622	0.0001598	0.0225343	0.5188481	25	PIM1/ZFP36L1/N4BP2L2/LTBP3/EIF2AK2/…
GOBP_KERATINOCYTE_PROLIFERATION	1.910881	0.8309192	0.0002265	0.0268445	0.5188481	13	AREG/ZFP36L1/ZFP36/KLF9/BCL11B/…
GOBP_REGULATION_OF_KERATINOCYTE_PROLIFERATION	1.901463	0.8636862	0.0002377	0.0273346	0.5188481	11	AREG/ZFP36L1/ZFP36/KLF9/BCL11B/…
GOBP_REGULATION_OF_STEM_CELL_PROLIFERATION	1.932395	0.7924327	0.0004497	0.0409968	0.4984931	17	PIM1/ZFP36L1/N4BP2L2/LTBP3/EIF2AK2/…
GOBP_ALCOHOL_CATABOLIC_PROCESS	-1.810971	-0.6549556	0.0019646	0.0926203	0.4317077	22	ALDH3B1/PTEN/GK/BPNT2/SYNJ1/…
GOBP_CEREBELLAR_CORTEX_DEVELOPMENT	-1.847074	-0.6464517	0.0017256	0.0837711	0.4550599	24	TTC21B/WNT7A/CLP1/HERC1/RORA/…
GOBP_EXCITATORY_SYNAPSE_ASSEMBLY	-1.825969	-0.8700692	0.0013714	0.0770854	0.4550599	7	PTEN/NPTN/WNT7A
GOBP_MITOCHONDRION_LOCALIZATION	-1.839853	-0.5959025	0.0012277	0.0758517	0.4550599	33	KAT2A/FEZ1/SLC4A5/LRPPRC/MFN1/…
GOBP_HINDBRAIN_MORPHOGENESIS	-1.880683	-0.6900828	0.0010563	0.0695336	0.4550599	20	TTC21B/WNT7A/HERC1/RORA/DAB1/…
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN_VIA_MHC_CLASS_IB	-1.725770	-0.9280066	0.0007618	0.0575309	0.4772708	5	TAP2/HLA-G
GOBP_N_TERMINAL_PROTEIN_AMINO_ACID_MODIFICATION	-1.925206	-0.6737969	0.0006795	0.0529149	0.4772708	24	HHAT/METAP1D/NAA30/NAA40/NAA15/…
GOBP_AXONEMAL_DYNEIN_COMPLEX_ASSEMBLY	-1.854941	-0.8636938	0.0004593	0.0413711	0.4984931	8	DNAAF10/DNAI2/DNAAF5
GOBP_LENS_DEVELOPMENT_IN_CAMERA_TYPE_EYE	-1.945022	-0.6627393	0.0002943	0.0305581	0.4984931	27	HIPK1/CRYBG3/SPRY1/WNT7A/TGFBR1/…
GOBP_INOSITOL_PHOSPHATE_METABOLIC_PROCESS	-1.988218	-0.6906353	0.0002464	0.0279149	0.4984931	25	PPIP5K1/PTEN/BPNT2/SYNJ1/INPP4A

11.1.3.2.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_HUMORAL_IMMUNE_RESPONSE	2.163674	0.6594244	0.0000001	0.0000942	0.7049757	130	CLU/RNASE2/S100A12/HLA-DRB1/S100A9/…
GOBP_ANTIMICROBIAL_HUMORAL_RESPONSE	2.153263	0.7718184	0.0000114	0.0040128	0.5933255	46	RNASE2/S100A12/S100A9/RNASE3/CXCL10/…
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_EXOGENOUS_PEPTIDE_ANTIGEN_VIA_MHC_CLASS_II	2.096229	0.8575825	0.0000522	0.0091739	0.5573322	23	HLA-DRB1/HLA-DPB1/CTSD/HLA-DPA1/HLA-DRA/…
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_EXOGENOUS_PEPTIDE_ANTIGEN	2.114744	0.8018881	0.0001064	0.0153056	0.5384341	32	HLA-DRB1/HLA-DPB1/CTSD/HLA-DPA1/HLA-DRA/…
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_EXOGENOUS_ANTIGEN	2.097620	0.7686913	0.0001359	0.0183338	0.5188481	39	HLA-DRB1/HLA-DPB1/CTSD/HLA-DPA1/HLA-DRA/…
GOBP_DEFENSE_RESPONSE_TO_FUNGUS	2.079787	0.8436805	0.0001465	0.0183338	0.5188481	24	S100A8/S100A12/S100A9/MPO/CX3CR1/…
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_OR_POLYSACCHARIDE_ANTIGEN_VIA_MHC_CLASS_II	2.065245	0.8114667	0.0001416	0.0183338	0.5188481	27	HLA-DRB1/HLA-DPB1/CTSD/HLA-DPA1/HLA-DRA/…
GOBP_IMMUNOGLOBULIN_PRODUCTION_INVOLVED_IN_IMMUNOGLOBULIN_MEDIATED_IMMUNE_RESPONSE	2.035406	0.6981273	0.0001513	0.0183338	0.5188481	57	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
GOBP_DEFENSE_RESPONSE_TO_GRAM_NEGATIVE_BACTERIUM	2.036333	0.7868104	0.0002425	0.0266826	0.5188481	29	RNASE2/RNASE3/H2BC11/CD4/MPEG1/…
GOBP_PEPTIDE_ANTIGEN_ASSEMBLY_WITH_MHC_PROTEIN_COMPLEX	2.066526	0.8862317	0.0004514	0.0438580	0.4984931	17	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
GOBP_MRNA_PROCESSING	-1.390379	-0.3320768	0.0007022	0.0571538	0.4772708	402	TENT4A/SRSF4/RRP1B/CDK11A/DHX15/…
GOBP_RNA_PROCESSING	-1.327048	-0.3035917	0.0000869	0.0134643	0.5384341	781	TENT4A/SRSF4/PTCD1/METTL25B/RRP1B/…

11.1.3.2.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_PEPTIDE_ANTIGEN_ASSEMBLY_WITH_MHC_CLASS_II_PROTEIN_COMPLEX	2.327248	0.8866218	0.0000186	0.0229055	0.5756103	14	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
GOBP_PEPTIDE_ANTIGEN_ASSEMBLY_WITH_MHC_PROTEIN_COMPLEX	2.307622	0.8416140	0.0000307	0.0289097	0.5573322	18	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
GOBP_DEFENSE_RESPONSE_TO_FUNGUS	2.280060	0.8686447	0.0000660	0.0430338	0.5384341	14	S100A8/S100A9/S100A12/CX3CR1
GOBP_NEUTROPHIL_CHEMOTAXIS	2.181337	0.7034697	0.0000832	0.0458773	0.5384341	33	S100A8/S100A9/S100A12/CD74
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_EXOGENOUS_PEPTIDE_ANTIGEN_VIA_MHC_CLASS_II	2.221043	0.7725118	0.0001179	0.0497257	0.5384341	22	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_EXOGENOUS_ANTIGEN	2.096504	0.6612864	0.0001518	0.0604769	0.5188481	37	HLA-DPB1/HLA-DQA1/CD1C/HLA-DRB1/HLA-DPA1/…
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_EXOGENOUS_PEPTIDE_ANTIGEN	2.129781	0.6980586	0.0002221	0.0725941	0.5188481	30	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_OR_POLYSACCHARIDE_ANTIGEN_VIA_MHC_CLASS_II	2.147929	0.7386930	0.0002888	0.0767070	0.4984931	24	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
GOBP_RESPONSE_TO_FUNGUS	2.197113	0.7588724	0.0003386	0.0793038	0.4984931	23	S100A8/S100A9/S100A12/CX3CR1
GOBP_REGULATION_OF_VIRAL_ENTRY_INTO_HOST_CELL	2.112268	0.7346781	0.0005561	0.0972755	0.4772708	22	IFITM3/HLA-DRB1/IFITM1/CD74/LY6E/…
GOBP_RESPONSE_TO_NERVE_GROWTH_FACTOR	-1.742844	-0.7272221	0.0004195	0.0869578	0.4984931	20	ACAP2/EIF4A3/ARF6/KIDINS220/APP/…
GOBP_TOOTH_MINERALIZATION	-1.560006	-0.9840261	0.0003538	0.0793038	0.4984931	3	TCIRG1
GOBP_VITAMIN_D_RECEPTOR_SIGNALING_PATHWAY	-1.626882	-0.9634434	0.0002885	0.0767070	0.4984931	4	RXRA/SNW1
GOBP_REGULATION_OF_RESPONSE_TO_EXTRACELLULAR_STIMULUS	-1.686575	-0.9469819	0.0002329	0.0725941	0.5188481	5	RXRA/SNW1

11.1.3.3 GO_MF

11.1.3.3.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.3.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.3.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.1.3.3.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_CYTOSKELETAL_MOTOR_ACTIVITY	1.986712	0.7037084	0.0000251	0.0167721	0.5756103	51	MYL6B/KIF23/KIFC1/CENPE/KIF2C/…
GOMF_MICROTUBULE_MOTOR_ACTIVITY	1.974092	0.7303336	0.0000292	0.0167721	0.5756103	38	KIF23/KIFC1/CENPE/KIF2C/KIF15/…
GOMF_MODIFIED_AMINO_ACID_BINDING	1.951044	0.7127251	0.0000401	0.0167721	0.5573322	42	TYMS/DHFR/UROS/SYTL2/PTGES2/…
GOMF_SINGLE_STRANDED_DNA_BINDING	1.832294	0.5844342	0.0000303	0.0167721	0.5756103	110	MCM4/CDC45/MCM7/MCM2/RAD51/…
GOMF_CYCLIN_DEPENDENT_PROTEIN_SERINE_THREONINE_KINASE_REGULATOR_ACTIVITY	1.944380	0.7381697	0.0002052	0.0687032	0.5188481	33	CKS2/CCNB2/CCNA2/CCNB1/CDKN1B/…
GOMF_CYSTEINE_TYPE_ENDOPEPTIDASE_INHIBITOR_ACTIVITY	1.900817	0.7730034	0.0005785	0.0811981	0.4772708	24	BIRC5/PTTG1/TNFSF14/PRDX3/XIAP/…
GOMF_TRANSLATION_REPRESSOR_ACTIVITY_MRNA_REGULATORY_ELEMENT_BINDING	1.865708	0.8725790	0.0004862	0.0811981	0.4984931	12	TYMS/DHFR/SHMT2
GOMF_DNA_REPLICATION_ORIGIN_BINDING	1.826807	0.8264052	0.0005821	0.0811981	0.4772708	15	CDC45/MCM2/MCM10/MCM5/HSPD1/…
GOMF_SINGLE_STRANDED_DNA_HELICASE_ACTIVITY	1.854026	0.7590126	0.0011215	0.0938730	0.4550599	23	MCM4/MCM7/MCM2/RAD51/MCM5/…
GOMF_TRANSLATION_REPRESSOR_ACTIVITY	1.845158	0.7867681	0.0012523	0.0998226	0.4550599	19	TYMS/DHFR/SHMT2
GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME	-1.498186	-0.3895763	0.0009113	0.0897317	0.4772708	149	RPS4X/MRPL2/RPL11/RPL10A/RPS23/…
GOMF_CATALYTIC_ACTIVITY_ACTING_ON_RNA	-1.382495	-0.3248788	0.0006476	0.0833946	0.4772708	330	EXOG/CNOT8/DDX46/FARSB/APEX1/…
GOMF_TELOMERASE_INHIBITOR_ACTIVITY	-1.591999	-0.9807853	0.0004116	0.0811981	0.4984931	3	ERCC4/PIF1

11.1.3.3.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_IMMUNOGLOBULIN_RECEPTOR_BINDING	2.223378	0.6808708	3.00e-07	0.0004202	0.6749629	61	IGHV3-72/IGHV3-49/IGHV6-1/IGHV7-81/IGHV2-70/…
GOMF_ANTIGEN_BINDING	1.865352	0.5144477	1.83e-05	0.0101870	0.5756103	128	IGHV3-72/IGHV3-49/IGHV6-1/IGHV7-81/IGHV2-70/…
GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME	-1.979670	-0.5056111	1.00e-06	0.0008148	0.6435518	150	RPL41/RPS15A/RPS16/RPL37A/MRPS23/…

11.1.3.3.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_SIGNALING_RECEPTOR_REGULATOR_ACTIVITY	2.032457	0.5963079	0.0000007	0.0005489	0.6594444	100	AREG/IL16/OSM/IL32/CD40LG/…
GOMF_TUMOR_NECROSIS_FACTOR_ACTIVATED_RECEPTOR_ACTIVITY	1.718074	0.9968417	0.0000004	0.0005489	0.6749629	4	TNFRSF4/TNFRSF18
GOMF_CYTOKINE_ACTIVITY	2.161221	0.7274334	0.0000022	0.0012280	0.6272567	42	IL16/OSM/IL32/CD40LG/GREM2/…
GOMF_CATALYTIC_ACTIVITY_ACTING_ON_A_NUCLEIC_ACID	-1.364325	-0.2998334	0.0004322	0.0902781	0.4984931	493	DHX8/ALKBH1/POLR1E/AGO1/FTO/…
GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME	-1.587901	-0.4017302	0.0002475	0.0590874	0.4984931	149	RPLP2/RPS7/RPL35/MRPS33/MRPS14/…
GOMF_INOSITOL_HEXAKISPHOSPHATE_KINASE_ACTIVITY	-1.769559	-0.9491201	0.0001786	0.0497426	0.5188481	5	PPIP5K1
GOMF_1_PHOSPHATIDYLINOSITOL_4_KINASE_ACTIVITY	-1.607687	-0.9900341	0.0001210	0.0404335	0.5384341	3	PI4KA/PI4KB

11.1.3.3.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_MHC_PROTEIN_COMPLEX_BINDING	2.048490	0.7798483	0.0001983	0.0349329	0.5188481	30	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
GOMF_MHC_CLASS_II_PROTEIN_COMPLEX_BINDING	2.041239	0.8134498	0.0001137	0.0349329	0.5384341	24	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
GOMF_ENDONUCLEASE_ACTIVITY_ACTIVE_WITH_EITHER_RIBO_OR_DEOXYRIBONUCLEIC_ACIDS_AND_PRODUCING_3_PHOSPHOMONOESTERS	1.989349	0.9231102	0.0000922	0.0349329	0.5384341	12	RNASE2/RNASE1/RNASE4
GOMF_CALCIUM_ION_BINDING	1.763735	0.5082036	0.0000783	0.0349329	0.5384341	219	S100A8/S100A9/DYSF/ASPH/NOTCH2/…
GOMF_PEPTIDE_BINDING	1.742342	0.5194872	0.0001907	0.0349329	0.5188481	164	CLU/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/…
GOMF_MISFOLDED_PROTEIN_BINDING	1.988678	0.7831213	0.0002979	0.0453585	0.4984931	25	CLU/SDF2L1/HSPA5/DNAJB11/DNAJC10
GOMF_SIGNALING_RECEPTOR_REGULATOR_ACTIVITY	1.831525	0.5684759	0.0006799	0.0875983	0.4772708	111	RETN/ADM/CXCL10/IL1B/CXCL8/…
GOMF_IMMUNE_RECEPTOR_ACTIVITY	1.804870	0.6002018	0.0008141	0.0880927	0.4772708	70	FCGR1A/HLA-DRB1/HLA-DPA1/HLA-DRA/CX3CR1/…
GOMF_PEPTIDE_ANTIGEN_BINDING	1.943032	0.7944813	0.0009100	0.0896624	0.4772708	21	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DQB1/…
GOMF_ELECTRON_TRANSFER_ACTIVITY	1.791593	0.5657850	0.0010685	0.0994256	0.4550599	100	ASPH/MT-ND6/CYC1/NDUFB9/ETFB/…

11.1.3.3.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_RAGE_RECEPTOR_BINDING	2.112261	0.9811787	0.0000035	0.0018163	0.6272567	6	S100A8/S100A12/HMGB2
GOMF_MHC_CLASS_II_RECEPTOR_ACTIVITY	2.263292	0.9449148	0.0000056	0.0021814	0.6105269	9	HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/HLA-DQB1/…
GOMF_MHC_CLASS_II_PROTEIN_COMPLEX_BINDING	2.387379	0.8256735	0.0000119	0.0037266	0.5933255	23	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
GOMF_MHC_PROTEIN_COMPLEX_BINDING	2.317274	0.7720742	0.0000212	0.0055387	0.5756103	27	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
GOMF_IMMUNE_RECEPTOR_ACTIVITY	2.172245	0.6511305	0.0000290	0.0064749	0.5756103	48	HLA-DQA1/FCGR1A/IFNGR2/HLA-DRB1/HLA-DPA1/…
GOMF_FATTY_ACID_BINDING	2.353652	0.8792839	0.0000439	0.0085849	0.5573322	15	S100A8/S100A9/PTGDS
GOMF_TOLL_LIKE_RECEPTOR_BINDING	2.211241	0.8792482	0.0000511	0.0088869	0.5573322	11	S100A8/S100A9/TLR1
GOMF_ANTIGEN_BINDING	2.131335	0.6375734	0.0001354	0.0201226	0.5188481	47	HLA-DPB1/HLA-DQA1/CD1C/HLA-DRB1/HLA-DPA1/…
GOMF_LIPOPEPTIDE_BINDING	2.093977	0.9369010	0.0001414	0.0201226	0.5188481	7	CD1C/CD14/CD1D/CD1E
GOMF_PEPTIDE_ANTIGEN_BINDING	2.084922	0.7721010	0.0004730	0.0569361	0.4984931	16	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
GOMF_PROTON_TRANSMEMBRANE_TRANSPORTER_ACTIVITY	-1.589458	-0.5593554	0.0006385	0.0713799	0.4772708	65	TCIRG1/UQCRFS1/ATP6V1G1/UQCR10/DMAC2L/…

11.1.3.4 GO_CC

11.1.3.4.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.4.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.4.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.1.3.4.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_CHROMOSOMAL_REGION	2.123823	0.6151169	0.0000000	0.0000000	0.9653278	295	BIRC5/CENPM/MCM4/TOP2A/CENPF/…
GOCC_CONDENSED_CHROMOSOME	2.234498	0.6674972	0.0000000	0.0000000	0.9545416	199	MKI67/BIRC5/CENPM/TOP2A/CENPF/…
GOCC_NUCLEAR_CHROMOSOME	2.139723	0.6447094	0.0000000	0.0000000	0.8390889	180	BIRC5/MCM4/H3C2/TOP2A/PLK1/…
GOCC_CONDENSED_CHROMOSOME_CENTROMERIC_REGION	2.147046	0.6638263	0.0000000	0.0000001	0.8140358	143	BIRC5/CENPM/CENPF/CENPU/PLK1/…
GOCC_CHROMOSOME_CENTROMERIC_REGION	2.036108	0.6205074	0.0000000	0.0000036	0.7337620	161	BIRC5/CENPM/CENPF/CENPU/PLK1/…
GOCC_MITOTIC_SPINDLE	1.954834	0.6148481	0.0000008	0.0000761	0.6594444	124	TPX2/PLK1/CKAP2L/CDK1/MAD2L1/…
GOCC_DNA_PACKAGING_COMPLEX	2.007591	0.6538712	0.0000015	0.0001256	0.6435518	92	H3C2/H2AZ1/H4C3/H1-2/NCAPG/…
GOCC_SPINDLE_POLE	1.933980	0.6001730	0.0000021	0.0001566	0.6272567	137	CDC20/CENPF/TPX2/PLK1/CKAP2L/…
GOCC_CYCLIN_DEPENDENT_PROTEIN_KINASE_HOLOENZYME_COMPLEX	2.005038	0.7465879	0.0000299	0.0013370	0.5756103	39	PCNA/CKS2/CCNB2/CDK1/CCNA2/…
GOCC_T_CELL_RECEPTOR_COMPLEX	1.966689	0.6708984	0.0000287	0.0013370	0.5756103	67	TRAV38-2DV8/TRGV5/TRAV1-1/TRBV20-1/TRBV7-9/…
GOCC_RIBOSOMAL_SUBUNIT	-1.559063	-0.3980319	0.0006768	0.0190078	0.4772708	161	HBA1/RPS4X/MRPL2/RPL11/RPL10A/…
GOCC_AIM2_INFLAMMASOME_COMPLEX	-1.657264	-0.9593314	0.0005532	0.0163421	0.4772708	4	CASP4
GOCC_CYTOSOLIC_RIBOSOME	-1.715178	-0.4735702	0.0004986	0.0158102	0.4772708	90	HBA1/RPS4X/RPL11/RPL10A/RPS23/…
GOCC_RIBOSOME	-1.545687	-0.3855265	0.0002511	0.0094941	0.4984931	192	HBA1/RPS4X/MRPL2/RPL11/APEX1/…
GOCC_POLYSOME	-1.841048	-0.5500895	0.0002322	0.0091308	0.5188481	58	RPS4X/RPL11/MCRS1/RPL10A/RPS23/…

11.1.3.4.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_IMMUNOGLOBULIN_COMPLEX	2.488389	0.6890838	0.0000000	0.0000000	0.9865463	124	IGLV7-46/IGLV3-9/IGHV3-72/IGHV3-49/IGLV2-18/…
GOCC_IMMUNOGLOBULIN_COMPLEX_CIRCULATING	2.234906	0.6869169	0.0000003	0.0001321	0.6749629	61	IGHV3-72/IGHV3-49/IGHV6-1/IGHV7-81/IGHV2-70/…
GOCC_TRANSCRIPTION_FACTOR_AP_1_COMPLEX	1.788564	0.9770407	0.0000151	0.0024897	0.5933255	5	JUN/JUND/FOS/JUNB
GOCC_EXTERNAL_SIDE_OF_PLASMA_MEMBRANE	1.636280	0.4250024	0.0001710	0.0188108	0.5188481	211	CD69/IGHV3-72/IGHV3-49/IGHV6-1/S1PR1/…
GOCC_CELL_SURFACE	1.425334	0.3504498	0.0007571	0.0681356	0.4772708	362	CD69/IGHV3-72/IGHV3-49/IGHV6-1/S1PR1/…
GOCC_MEMBRANE_PROTEIN_COMPLEX	-1.349865	-0.2978227	0.0004103	0.0406191	0.4984931	662	NDUFS3/LIN7C/DNAJC11/HLA-DQA1/NCF4/…
GOCC_RIBOSOME	-1.708962	-0.4277936	0.0000306	0.0037919	0.5573322	192	RPL41/MRPL45/MRPL38/RPS15A/RPS16/…
GOCC_CYTOSOLIC_RIBOSOME	-1.910424	-0.5256877	0.0000258	0.0036451	0.5756103	91	RPL41/RPS15A/RPS16/RPL37A/RPL29/…
GOCC_LARGE_RIBOSOMAL_SUBUNIT	-1.948565	-0.5290340	0.0000053	0.0011309	0.6105269	104	RPL41/MRPL45/MRPL38/RPL29/UBA52/…
GOCC_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT	-2.069381	-0.6357955	0.0000057	0.0011309	0.6105269	50	RPL41/RPL37A/RPL29/UBA52/RPL34/…
GOCC_RIBOSOMAL_SUBUNIT	-1.916937	-0.4893259	0.0000009	0.0002953	0.6594444	161	RPL41/MRPL45/MRPL38/RPS15A/RPS16/…

11.1.3.4.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_CELL_SURFACE	1.735673	0.4506186	0.0000002	0.0002110	0.6901325	357	CD69/TNFRSF4/AREG/TNFRSF18/FLOT1/…
GOCC_MEMBRANE_MICRODOMAIN	1.805244	0.5081181	0.0000083	0.0040672	0.5933255	154	FLOT1/TNFRSF1A/HMOX1/SLC25A5/ADTRP/…
GOCC_EXTERNAL_SIDE_OF_PLASMA_MEMBRANE	1.711477	0.4659494	0.0000205	0.0067246	0.5756103	212	CD69/TNFRSF4/TNFRSF18/FLOT1/LAG3/…
GOCC_T_CELL_RECEPTOR_COMPLEX	1.753329	0.5217590	0.0001415	0.0278002	0.5188481	98	TRBV6-6/TRBV7-7/TRGV2/TRAV13-1/TRBV11-1/…
GOCC_TRANSCRIPTION_FACTOR_AP_1_COMPLEX	1.686755	0.9375073	0.0004197	0.0515227	0.4984931	5	FOS/JUN/JUNB
GOCC_CORTICAL_CYTOSKELETON	1.763539	0.5885794	0.0006032	0.0658159	0.4772708	49	CLDN5/FLOT1/GYPC/MYADM/COTL1/…
GOCC_TERTIARY_GRANULE_MEMBRANE	1.791363	0.6048151	0.0009715	0.0954010	0.4772708	46	CEACAM1/CYBA/TRPM2/SLC2A3/FCER1G/…
GOCC_AMPA_GLUTAMATE_RECEPTOR_COMPLEX	1.651277	0.9177881	0.0013888	0.0956082	0.4550599	5	ABHD12/OLFM2/DLG3
GOCC_ANNULATE_LAMELLAE	1.648001	0.9159676	0.0015081	0.0956082	0.4550599	5	EIF5A/XPO1/TNPO3
GOCC_CORTICAL_ACTIN_CYTOSKELETON	1.723687	0.6035615	0.0016765	0.0968414	0.4550599	37	CLDN5/FLOT1/MYADM/COTL1/CAPN2/…
GOCC_ENDOPLASMIC_RETICULUM_TUBULAR_NETWORK_MEMBRANE	-1.633336	-0.9499817	0.0015578	0.0956082	0.4550599	4	ATL3/LNPK
GOCC_ALPHA_DNA_POLYMERASE_PRIMASE_COMPLEX	-1.640033	-0.9538771	0.0013196	0.0956082	0.4550599	4	POLA1/PRIM2
GOCC_CYTOSOLIC_RIBOSOME	-1.671907	-0.4522215	0.0012793	0.0956082	0.4550599	88	RPLP2/RPS7/RPL35/RPL6/RPL23A/…

11.1.3.4.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_VESICLE_LUMEN	2.078219	0.5956180	0.0000000	0.0000010	0.7614608	235	CLU/RETN/HP/RNASE2/S100A8/…
GOCC_EXTERNAL_ENCAPSULATING_STRUCTURE	2.173879	0.6683608	0.0000003	0.0000567	0.6749629	117	CLU/S100A8/S100A9/CTSD/LGALS1/…
GOCC_VACUOLAR_LUMEN	2.116920	0.6518459	0.0000013	0.0002170	0.6435518	115	RETN/RNASE2/PLAC8/CTSD/MPO/…
GOCC_CLATHRIN_COATED_ENDOCYTIC_VESICLE_MEMBRANE	2.075834	0.7476886	0.0000235	0.0023517	0.5756103	41	FCGR1A/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/…
GOCC_COLLAGEN_CONTAINING_EXTRACELLULAR_MATRIX	2.036634	0.6476584	0.0000216	0.0023517	0.5756103	95	S100A8/S100A9/CTSD/LGALS1/FGL2/…
GOCC_MHC_CLASS_II_PROTEIN_COMPLEX	1.984915	0.8989870	0.0000564	0.0046474	0.5573322	14	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
GOCC_BLOOD_MICROPARTICLE	1.983022	0.6843357	0.0001224	0.0093110	0.5188481	54	CLU/HP/PFN1/IGLV3-21/ACTB/…
GOCC_CLATHRIN_COATED_ENDOCYTIC_VESICLE	1.977163	0.6870066	0.0002900	0.0179254	0.4984931	52	FCGR1A/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/…
GOCC_LUMENAL_SIDE_OF_MEMBRANE	1.978420	0.7665233	0.0003744	0.0217799	0.4984931	28	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DQB1/…
GOCC_MHC_PROTEIN_COMPLEX	1.985914	0.8095036	0.0005036	0.0262136	0.4772708	22	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
GOCC_STEREOCILIUM_BUNDLE	-2.048812	-0.7930946	0.0000304	0.0027319	0.5756103	20	MYO1C/FCHSD2/TRIOBP/ELMOD3/PAFAH1B1

11.1.3.4.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_CYTOSOLIC_RIBOSOME	2.552043	0.6868500	0.0000000	0.0000000	0.8634154	88	RPS3A/RPS15A/EIF2A/RPL4/RPL8/…
GOCC_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT	2.435980	0.7259745	0.0000002	0.0000822	0.6901325	48	RPL4/RPL10/RPL8/RPL34/RPL26/…
GOCC_CLATHRIN_COATED_ENDOCYTIC_VESICLE_MEMBRANE	2.491602	0.8109419	0.0000003	0.0000862	0.6749629	30	AREG/HLA-DPB1/HLA-DQA1/FCGR1A/HLA-DRB1/…
GOCC_CLATHRIN_COATED_VESICLE_MEMBRANE	2.414883	0.7280867	0.0000015	0.0003474	0.6435518	45	AREG/HLA-DPB1/HLA-DQA1/FCGR1A/HLA-DRB1/…
GOCC_CLATHRIN_COATED_ENDOCYTIC_VESICLE	2.414769	0.7491360	0.0000026	0.0004912	0.6272567	37	AREG/HLA-DPB1/HLA-DQA1/FCGR1A/HLA-DRB1/…
GOCC_MHC_CLASS_II_PROTEIN_COMPLEX	2.389169	0.8919006	0.0000128	0.0015205	0.5933255	15	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
GOCC_MHC_PROTEIN_COMPLEX	2.181840	0.7628724	0.0002100	0.0132288	0.5188481	21	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
GOCC_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT	2.169121	0.6713940	0.0001849	0.0132288	0.5188481	38	RPS3A/RPS15A/EIF2A/RPS27A/ISG15/…
GOCC_ER_TO_GOLGI_TRANSPORT_VESICLE_MEMBRANE	2.160740	0.6572765	0.0001811	0.0132288	0.5188481	42	AREG/HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/…
GOCC_LUMENAL_SIDE_OF_MEMBRANE	2.144702	0.7323909	0.0009136	0.0435769	0.4772708	24	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
GOCC_INTRINSIC_COMPONENT_OF_POSTSYNAPTIC_MEMBRANE	-1.677693	-0.8459252	0.0016450	0.0634290	0.4550599	8	NPTN/CANX/CHRNB1/GABBR1
GOCC_U2_TYPE_SPLICEOSOMAL_COMPLEX	-1.522253	-0.5225778	0.0013099	0.0566509	0.4550599	82	CASC3/SNW1/CWC15/EIF4A3/TXNL4A/…
GOCC_PROTEASOME_COMPLEX	-1.587913	-0.5778117	0.0012897	0.0566509	0.4550599	50	PSMB5/PSMC3/PSMB8/PSMA5/PAAF1/…
GOCC_PEPTIDASE_COMPLEX	-1.592547	-0.5487597	0.0002357	0.0132288	0.5188481	79	SUPT20H/PSMB5/PSMC3/ENY2/PSMB8/…
GOCC_ENDOPEPTIDASE_COMPLEX	-1.642985	-0.5814710	0.0001667	0.0132288	0.5188481	62	PSMB5/PSMC3/PSMB8/PSMA5/CAPN2/…
GOCC_CATALYTIC_COMPLEX	-1.337776	-0.4097882	0.0000087	0.0013807	0.5933255	788	WWP2/CCNT1/CASC3/SNW1/FBXL15/…

11.1.3.5 CP_REACTOME

11.1.3.5.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.5.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.5.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.1.3.5.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_CELL_CYCLE	2.289754	0.6357001	0.0000000	0.0000000	1.5092628	604	TYMS/TK1/UBE2C/CDC20/BIRC5/…
REACTOME_CELL_CYCLE_MITOTIC	2.380680	0.6684330	0.0000000	0.0000000	1.4954793	474	TYMS/TK1/UBE2C/CDC20/BIRC5/…
REACTOME_CELL_CYCLE_CHECKPOINTS	2.288546	0.6742240	0.0000000	0.0000000	1.0476265	238	CDC20/CENPM/MCM4/CENPF/CLSPN/…
REACTOME_MITOTIC_G1_PHASE_AND_G1_S_TRANSITION	2.409149	0.7570970	0.0000000	0.0000000	1.0175448	122	TYMS/TK1/MCM4/TOP2A/RRM2/…
REACTOME_MITOTIC_METAPHASE_AND_ANAPHASE	2.229852	0.6634384	0.0000000	0.0000000	0.9759947	215	UBE2C/CDC20/BIRC5/CENPM/CENPF/…
REACTOME_RESOLUTION_OF_SISTER_CHROMATID_COHESION	2.353102	0.7536475	0.0000000	0.0000000	0.9436322	109	CDC20/BIRC5/CENPM/CENPF/CENPU/…
REACTOME_SEPARATION_OF_SISTER_CHROMATIDS	2.242889	0.6828669	0.0000000	0.0000000	0.9325952	171	UBE2C/CDC20/BIRC5/CENPM/CENPF/…
REACTOME_MITOTIC_PROMETAPHASE	2.229588	0.6735329	0.0000000	0.0000000	0.9214260	184	CDC20/BIRC5/CENPM/CENPF/CENPU/…
REACTOME_MITOTIC_SPINDLE_CHECKPOINT	2.311261	0.7428671	0.0000000	0.0000000	0.8986712	104	UBE2C/CDC20/BIRC5/CENPM/CENPF/…
REACTOME_RHO_GTPASES_ACTIVATE_FORMINS	2.230452	0.7034458	0.0000000	0.0000000	0.8266573	119	CDC20/BIRC5/CENPM/CENPF/CENPU/…
REACTOME_SYNTHESIS_OF_BILE_ACIDS_AND_BILE_SALTS	-1.734441	-0.6925158	0.0064504	0.0685872	0.4070179	14	AKR1C3/OSBPL9/NCOA1/SCP2
REACTOME_ACYL_CHAIN_REMODELLING_OF_PE	-1.757069	-0.7851604	0.0056879	0.0639073	0.4070179	10	PLBD1/ABHD4/PLA2G12A/PLA2G6/LPCAT3
REACTOME_ACTIVATION_OF_THE_MRNA_UPON_BINDING_OF_THE_CAP_BINDING_COMPLEX_AND_EIFS_AND_SUBSEQUENT_BINDING_TO_43S	-1.710116	-0.5106442	0.0055110	0.0623656	0.4070179	52	RPS4X/EIF3G/RPS23/EIF3D/RPS5/…
REACTOME_SLC_TRANSPORTER_DISORDERS	-1.714183	-0.5072328	0.0047103	0.0544806	0.4070179	56	SLC29A3/TPR/NUP85/POM121/SLC22A5/…
REACTOME_REGULATION_OF_GLUCOKINASE_BY_GLUCOKINASE_REGULATORY_PROTEIN	-1.834728	-0.6171543	0.0030878	0.0416451	0.4317077	29	TPR/NUP85/POM121/NUP50/NDC1/…
REACTOME_SUMOYLATION_OF_SUMOYLATION_PROTEINS	-1.875133	-0.6100965	0.0016279	0.0256061	0.4550599	35	TPR/NUP85/POM121/PIAS4/NUP50/…
REACTOME_TRNA_PROCESSING_IN_THE_NUCLEUS	-1.776648	-0.5269802	0.0012447	0.0208296	0.4550599	58	TPR/NUP85/POM121/TSEN54/ELAC2/…
REACTOME_SUMOYLATION_OF_UBIQUITINYLATION_PROTEINS	-1.844694	-0.5917798	0.0008424	0.0155356	0.4772708	39	TPR/NUP85/POM121/PIAS4/NUP50/…
REACTOME_VIRAL_MESSENGER_RNA_SYNTHESIS	-1.921465	-0.5992917	0.0006187	0.0128047	0.4772708	42	TPR/NUP85/POM121/POLR2C/POLR2D/…
REACTOME_HEME_SIGNALING	-1.974960	-0.6382712	0.0001865	0.0048907	0.5188481	37	HBA1/SIRT1/NCOA1/CHD9/MEF2D/…

11.1.3.5.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_CREATION_OF_C4_AND_C2_ACTIVATORS	2.065086	0.6687077	0.0000078	0.0015564	0.5933255	54	IGHV1-46/IGHV2-70/IGLV2-23/IGHG1/IGKV1D-16/…
REACTOME_FCGR_ACTIVATION	2.052936	0.6418801	0.0000079	0.0015564	0.5933255	61	IGHV1-46/IGHV2-70/IGLV2-23/IGHG1/IGKV1D-16/…
REACTOME_FCGR3A_MEDIATED_IL10_SYNTHESIS	1.992534	0.5979952	0.0000068	0.0015564	0.6105269	77	IGHV1-46/IGHV2-70/IGLV2-23/IGHG1/IGKV1D-16/…
REACTOME_INITIAL_TRIGGERING_OF_COMPLEMENT	2.057428	0.6571009	0.0000097	0.0016881	0.5933255	57	IGHV1-46/IGHV2-70/IGLV2-23/IGHG1/IGKV1D-16/…
REACTOME_COMPLEMENT_CASCADE	2.001713	0.6180322	0.0000158	0.0024712	0.5756103	66	IGHV1-46/IGHV2-70/IGLV2-23/IGHG1/IGKV1D-16/…
REACTOME_ROLE_OF_LAT2_NTAL_LAB_ON_CALCIUM_MOBILIZATION	2.002822	0.6258283	0.0000207	0.0025407	0.5756103	63	IGHV1-46/PDPK1/IGHV2-70/IGLV2-23/IGKV1D-16/…
REACTOME_FCERI_MEDIATED_MAPK_ACTIVATION	1.979456	0.5991667	0.0000180	0.0025407	0.5756103	72	JUN/IGHV1-46/FOS/IGHV2-70/IGLV2-23/…
REACTOME_FCERI_MEDIATED_CA_2_MOBILIZATION	1.878472	0.5645345	0.0000844	0.0066104	0.5384341	75	IGHV1-46/IGHV2-70/IGLV2-23/IGKV1D-16/IGKV1D-12/…
REACTOME_ROLE_OF_PHOSPHOLIPIDS_IN_PHAGOCYTOSIS	1.856106	0.5610546	0.0001777	0.0132240	0.5188481	73	IGHV1-46/IGHV2-70/IGLV2-23/IGHG1/IGKV1D-16/…
REACTOME_SCAVENGING_OF_HEME_FROM_PLASMA	1.884746	0.6155340	0.0003831	0.0240119	0.4984931	50	IGHV1-46/IGHV2-70/IGKV1D-16/IGKV1D-12/IGHV4-34/…
REACTOME_MICRORNA_MIRNA_BIOGENESIS	-1.875600	-0.6782889	0.0017554	0.0859580	0.4550599	23	AGO2/POLR2K/POLR2D/POLR2G/XPO5/…
REACTOME_RESPONSE_OF_EIF2AK4_GCN2_TO_AMINO_ACID_DEFICIENCY	-1.889937	-0.5088080	0.0000572	0.0049789	0.5573322	100	RPL41/RPS15A/RPS16/TRIB3/RPL37A/…
REACTOME_EUKARYOTIC_TRANSLATION_INITIATION	-1.924067	-0.5220484	0.0000507	0.0047665	0.5573322	95	RPL41/RPS15A/EIF3K/RPS16/RPL37A/…
REACTOME_PIWI_INTERACTING_RNA_PIRNA_BIOGENESIS	-2.039703	-0.8056334	0.0000517	0.0047665	0.5573322	16	POLR2K/HSP90AA1/PLD6/POLR2D/HENMT1/…
REACTOME_SELENOAMINO_ACID_METABOLISM	-1.930393	-0.5204926	0.0000211	0.0025407	0.5756103	99	RPL41/RPS15A/RPS16/RPL37A/RPL29/…
REACTOME_NONSENSE_MEDIATED_DECAY_NMD	-1.954446	-0.5186864	0.0000046	0.0014550	0.6105269	111	RPL41/RPS15A/RPS16/RPL37A/RPL29/…
REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION	-2.064545	-0.5685296	0.0000038	0.0014550	0.6272567	87	RPL41/RPS15A/RPS16/RPL37A/RPL29/…
REACTOME_INFLUENZA_INFECTION	-1.931008	-0.4911956	0.0000018	0.0010311	0.6435518	150	KPNA4/RPL41/POLR2K/HSP90AA1/RPS15A/…
REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE	-1.999921	-0.5349662	0.0000020	0.0010311	0.6272567	108	RPL41/SPCS2/RPS15A/SSR3/RPS16/…
REACTOME_TRANSLATION	-1.822634	-0.4332126	0.0000004	0.0006434	0.6749629	279	RPL41/MRPL45/PTCD3/TRMT112/MRPL38/…

11.1.3.5.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_INTERLEUKIN_4_AND_INTERLEUKIN_13_SIGNALING	2.294714	0.7275841	0.0000000	0.0000016	0.7881868	61	PIM1/SOCS3/OSM/FOS/SOCS1/…
REACTOME_NGF_STIMULATED_TRANSCRIPTION	2.171093	0.8327271	0.0000005	0.0002587	0.6594444	23	SGK1/ID3/FOS/JUNB/EGR1
REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM	1.644510	0.4147830	0.0000005	0.0002587	0.6594444	485	PIM1/TNFRSF4/SOCS3/NFKBIA/IFNGR2/…
REACTOME_SIGNALING_BY_INTERLEUKINS	1.752203	0.4552325	0.0000023	0.0009181	0.6272567	299	PIM1/SOCS3/NFKBIA/SOCS2/IL16/…
REACTOME_TNFS_BIND_THEIR_PHYSIOLOGICAL_RECEPTORS	1.971908	0.8704376	0.0000454	0.0142974	0.5573322	11	TNFRSF4/TNFRSF18/TNFRSF1A/EDA
REACTOME_RUNX1_REGULATES_EXPRESSION_OF_COMPONENTS_OF_TIGHT_JUNCTIONS	1.614547	0.9928643	0.0000952	0.0249627	0.5384341	3	CLDN5/CBFB
REACTOME_NUCLEAR_EVENTS_KINASE_AND_TRANSCRIPTION_FACTOR_ACTIVATION	1.907472	0.6571611	0.0002072	0.0465805	0.5188481	39	SGK1/ID3/FOS/JUNB/EGR1
REACTOME_SIGNALING_BY_BMP	1.941446	0.8074343	0.0003128	0.0615510	0.4984931	14	GREM2/UBE2D3/SMURF2/SMAD7/NOG/…

11.1.3.5.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_NEUTROPHIL_DEGRANULATION	1.913036	0.5267514	0.0000000	0.0000026	0.7749390	390	RETN/HP/RNASE2/S100A8/PLAC8/…
REACTOME_MHC_CLASS_II_ANTIGEN_PRESENTATION	2.136595	0.6914075	0.0000031	0.0009637	0.6272567	85	HLA-DRB1/HLA-DPB1/CTSD/HLA-DPA1/HLA-DRA/…
REACTOME_ANTIMICROBIAL_PEPTIDES	2.027888	0.8628127	0.0000105	0.0027300	0.5933255	20	CLU/S100A8/S100A9/RNASE3/CD4/…
REACTOME_TCR_SIGNALING	1.946730	0.6130937	0.0000589	0.0131838	0.5573322	105	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/NFKBIA/…
REACTOME_GENERATION_OF_SECOND_MESSENGER_MOLECULES	1.964273	0.7674385	0.0001058	0.0207243	0.5384341	29	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DQB1/…
REACTOME_ANTIGEN_PROCESSING_CROSS_PRESENTATION	1.919823	0.6190395	0.0001929	0.0335851	0.5188481	89	S100A8/FCGR1A/S100A9/PSMB6/PSME2/…
REACTOME_COSTIMULATION_BY_THE_CD28_FAMILY	1.960511	0.6829988	0.0003529	0.0480993	0.4984931	54	MAP3K8/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/…
REACTOME_INTERFERON_GAMMA_SIGNALING	1.905652	0.6396230	0.0003174	0.0480993	0.4984931	68	FCGR1A/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/…
REACTOME_PD_1_SIGNALING	1.960298	0.8610723	0.0006475	0.0676439	0.4772708	17	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DQB1/…
REACTOME_RHO_GTPASES_ACTIVATE_NADPH_OXIDASES	1.907998	0.8570680	0.0007417	0.0726438	0.4772708	14	S100A8/S100A9/RAC2/CYBA

11.1.3.5.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION	2.927848	0.7921139	0.0000000	0.0000000	1.1512205	85	RPS3A/EEF2/RPS15A/EEF1A1/RPL4/…
REACTOME_RESPONSE_OF_EIF2AK4_GCN2_TO_AMINO_ACID_DEFICIENCY	2.464335	0.6602788	0.0000000	0.0000001	0.8266573	92	RPS3A/RPS15A/RPL4/RPL10/RPL8/…
REACTOME_SELENOAMINO_ACID_METABOLISM	2.417585	0.6477531	0.0000000	0.0000003	0.8012156	92	RPS3A/RPS15A/PAPSS1/RPL4/RPL10/…
REACTOME_EUKARYOTIC_TRANSLATION_INITIATION	2.304713	0.6175108	0.0000000	0.0000070	0.7337620	92	RPS3A/EIF4B/EIF4A2/RPS15A/RPL4/…
REACTOME_IRAK4_DEFICIENCY_TLR2_4	2.435762	0.9374072	0.0000007	0.0001828	0.6594444	12	S100A8/S100A9/CD14/BTK
REACTOME_DISEASES_OF_IMMUNE_SYSTEM	2.385226	0.8406022	0.0000066	0.0009463	0.6105269	19	S100A8/S100A9/CD14/NFKB1
REACTOME_REGULATION_OF_TLR_BY_ENDOGENOUS_LIGAND	2.308998	0.9370186	0.0000069	0.0009463	0.6105269	10	S100A8/S100A9/CD14
REACTOME_PD_1_SIGNALING	2.269443	0.8676930	0.0000624	0.0052552	0.5384341	13	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
REACTOME_ANTIMICROBIAL_PEPTIDES	2.196307	0.8397303	0.0001972	0.0119500	0.5188481	13	S100A8/S100A9/CLU/CD4
REACTOME_GENERATION_OF_SECOND_MESSENGER_MOLECULES	2.162489	0.7621052	0.0004766	0.0169423	0.4984931	19	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
REACTOME_MATURATION_OF_SARS_COV_1_NUCLEOPROTEIN	-1.630307	-0.8722066	0.0035578	0.0550004	0.2798657	6	PARP4/SUMO1/PARP9
REACTOME_PYRUVATE_METABOLISM	-1.750079	-0.7666272	0.0005338	0.0179708	0.4772708	15	RXRA/MPC2/PDHX/PDPR/PDK4/…
REACTOME_REGULATION_OF_PTEN_STABILITY_AND_ACTIVITY	-1.654283	-0.5920429	0.0004374	0.0169423	0.4984931	55	WWP2/PSMB5/PSMC3/PSMB8/PSMA5/…
REACTOME_VISUAL_PHOTOTRANSDUCTION	-1.736885	-0.7713666	0.0003413	0.0149190	0.4984931	14	CALM1/DHRS3/LRP1/METAP1/PLB1/…
REACTOME_REGULATION_OF_RUNX3_EXPRESSION_AND_ACTIVITY	-1.633873	-0.5967284	0.0003154	0.0144806	0.4984931	47	PSMB5/PSMC3/PSMB8/PSMA5/UBC/…
REACTOME_PYRUVATE_METABOLISM_AND_CITRIC_ACID_TCA_CYCLE	-1.716785	-0.6733585	0.0003053	0.0144806	0.4984931	28	RXRA/MPC2/ACO2/SDHD/PDHX/…
REACTOME_ORC1_REMOVAL_FROM_CHROMATIN	-1.637401	-0.5904853	0.0002710	0.0136872	0.4984931	52	PSMB5/PSMC3/PSMB8/PSMA5/UBC/…
REACTOME_REGULATION_OF_RUNX2_EXPRESSION_AND_ACTIVITY	-1.685489	-0.6131684	0.0001194	0.0086168	0.5384341	48	PSMC3/PSMB8/PSMA5/UBC/NR3C1/…
REACTOME_G2_M_CHECKPOINTS	-1.634917	-0.5546823	0.0000368	0.0042912	0.5573322	89	YWHAG/PSMB5/PSMC3/PSMB8/PSMA5/…
REACTOME_CYTOPROTECTION_BY_HMOX1	-1.660849	-0.5613173	0.0000320	0.0040450	0.5573322	94	RXRA/PSMB5/PSMC3/NDUFA4/BACH1/…

11.1.3.6 CP_KEGG

11.1.3.6.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.6.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.6.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.1.3.6.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_CELL_CYCLE	2.368696	0.7573590	0.0000000	0.0000000	0.9653278	107	CDC20/PLK1/PTTG1/PCNA/CDC45/…
KEGG_P53_SIGNALING_PATHWAY	2.213721	0.7785267	0.0000000	0.0000009	0.7477397	56	RRM2/CCNB2/CHEK1/CDK1/PMAIP1/…
KEGG_OOCYTE_MEIOSIS	2.073790	0.6980852	0.0000008	0.0000466	0.6594444	76	CDC20/PLK1/PTTG1/PKMYT1/CCNB2/…
KEGG_DNA_REPLICATION	2.033468	0.7982402	0.0000359	0.0013342	0.5573322	30	MCM4/PCNA/MCM7/MCM2/FEN1/…
KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS	1.913613	0.6293824	0.0000315	0.0013342	0.5573322	86	H3C2/H2AZ1/H4C3/H2AX/H2AC16/…
KEGG_ONE_CARBON_POOL_BY_FOLATE	1.901350	0.8649804	0.0001015	0.0027555	0.5384341	15	TYMS/DHFR/SHMT2/MTHFD1/MTHFD2/…
KEGG_PYRIMIDINE_METABOLISM	1.862932	0.6292376	0.0001037	0.0027555	0.5384341	74	TYMS/TK1/RRM2/DUT/RRM1/…
KEGG_PROGESTERONE_MEDIATED_OOCYTE_MATURATION	1.881709	0.6527769	0.0002091	0.0048611	0.5188481	59	PLK1/PKMYT1/CCNB2/CDK1/MAD2L1/…
KEGG_PATHWAYS_IN_CANCER	1.598302	0.4809982	0.0008310	0.0171738	0.4772708	182	BIRC5/NFKBIA/RAD51/BAX/BRCA2/…
KEGG_PROTEASOME	1.770950	0.6571985	0.0016089	0.0299255	0.4550599	41	PSMD2/PSME2/PSMD3/PSMA4/POMP/…
KEGG_RIBOSOME	-1.582053	-0.4392298	0.0044593	0.0754020	0.4070179	84	RPS4X/RPL11/RPL10A/RPL6/RPS5/…

11.1.3.6.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_MAPK_SIGNALING_PATHWAY	1.601598	0.4354561	0.0009307	0.0865506	0.4772708	146	JUN/DUSP1/JUND/MYC/FOS/…
KEGG_RIBOSOME	-2.013517	-0.5595300	0.0000050	0.0009305	0.6105269	84	RPL41/RPS15A/RPS16/RPL37A/RPL29/…

11.1.3.6.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_JAK_STAT_SIGNALING_PATHWAY	2.004276	0.6090932	0.0000042	0.0007840	0.6105269	76	PIM1/SOCS3/IFNGR2/SOCS2/OSM/…
KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION	1.902784	0.5630419	0.0000187	0.0017260	0.5756103	94	TNFRSF4/IFNGR2/TNFRSF18/OSM/TNFRSF1A/…
KEGG_LEISHMANIA_INFECTION	1.878891	0.6215761	0.0002953	0.0182118	0.4984931	45	NFKBIA/IFNGR2/FOS/IL10/CYBA/…
KEGG_TYPE_II_DIABETES_MELLITUS	1.921674	0.7509114	0.0005875	0.0271702	0.4772708	20	SOCS3/SOCS2/SOCS1/PRKCZ/PIK3CD
KEGG_COLORECTAL_CANCER	1.817259	0.5895323	0.0012838	0.0395842	0.4550599	50	FOS/BAX/JUN/BIRC5/LEF1/…
KEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY	1.784819	0.5870618	0.0011168	0.0395842	0.4550599	47	SOCS3/NFKBIA/TNFRSF1A/TRADD/ADIPOR1/…
KEGG_PARKINSONS_DISEASE	1.597732	0.4682974	0.0025029	0.0661474	0.4317077	104	MT-ND3/MT-ND1/MT-ATP6/MT-CYB/ATP5F1D/…

11.1.3.6.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_LEISHMANIA_INFECTION	2.270116	0.7998974	0.0000006	0.0001141	0.6594444	45	FCGR1A/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/…
KEGG_INTESTINAL_IMMUNE_NETWORK_FOR_IGA_PRODUCTION	2.232035	0.8706325	0.0000056	0.0005193	0.6105269	25	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
KEGG_ALLOGRAFT_REJECTION	2.118426	0.8310802	0.0000346	0.0012349	0.5573322	24	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
KEGG_ASTHMA	2.115794	0.9220639	0.0000304	0.0012349	0.5756103	14	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
KEGG_CELL_ADHESION_MOLECULES_CAMS	2.106790	0.7173294	0.0000398	0.0012349	0.5573322	56	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
KEGG_ANTIGEN_PROCESSING_AND_PRESENTATION	2.104295	0.7213498	0.0000510	0.0013560	0.5573322	53	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
KEGG_GRAFT_VERSUS_HOST_DISEASE	2.126954	0.8296444	0.0001305	0.0025492	0.5188481	25	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
KEGG_VIRAL_MYOCARDITIS	2.123395	0.7556473	0.0001374	0.0025492	0.5188481	42	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
KEGG_TYPE_I_DIABETES_MELLITUS	2.120448	0.8271065	0.0001508	0.0025492	0.5188481	25	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…
KEGG_AUTOIMMUNE_THYROID_DISEASE	2.107792	0.8550304	0.0002320	0.0035955	0.5188481	20	HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DRA/HLA-DMB/…

11.1.3.6.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_RIBOSOME	2.940224	0.7816192	0.0000000	0.0000000	1.1146645	84	RPS3A/RPS15A/RPL4/RPL10/RPL8/…
KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS	2.411273	0.7243773	0.0000005	0.0000478	0.6594444	41	HLA-DPB1/HLA-DQA1/FCGR1A/C1QB/HLA-DRB1/…
KEGG_LEISHMANIA_INFECTION	2.284199	0.6862026	0.0000045	0.0002737	0.6105269	41	HLA-DPB1/HLA-DQA1/FCGR1A/IFNGR2/HLA-DRB1/…
KEGG_ASTHMA	2.286285	0.8642071	0.0000114	0.0004187	0.5933255	15	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
KEGG_INTESTINAL_IMMUNE_NETWORK_FOR_IGA_PRODUCTION	2.276805	0.7954074	0.0000096	0.0004187	0.5933255	22	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
KEGG_CELL_ADHESION_MOLECULES_CAMS	2.155025	0.6473973	0.0000356	0.0010911	0.5573322	41	HLA-DPB1/HLA-DQA1/VCAN/HLA-DRB1/HLA-DPA1/…
KEGG_AUTOIMMUNE_THYROID_DISEASE	2.164303	0.7859898	0.0001654	0.0043481	0.5188481	18	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
KEGG_ALLOGRAFT_REJECTION	2.088005	0.7454467	0.0005937	0.0121386	0.4772708	21	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
KEGG_TYPE_I_DIABETES_MELLITUS	2.069722	0.7389193	0.0007541	0.0138750	0.4772708	21	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
KEGG_VIRAL_MYOCARDITIS	2.083402	0.6130263	0.0011348	0.0160614	0.4550599	37	HLA-DPB1/HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DRA/…
KEGG_VIBRIO_CHOLERAE_INFECTION	-1.536724	-0.6022936	0.0102672	0.0992129	0.3807304	28	TCIRG1/ATP6V1G1/PDIA4/GNAS/ATP6V1C1/…
KEGG_CITRATE_CYCLE_TCA_CYCLE	-1.555799	-0.6333197	0.0108629	0.0992129	0.3807304	22	ACO2/SDHD/OGDH/IDH3A/SDHB/…
KEGG_ALZHEIMERS_DISEASE	-1.378605	-0.4562842	0.0085468	0.0873675	0.3807304	111	UQCRFS1/SDHD/NDUFA4/UQCR10/CALM1/…
KEGG_EPITHELIAL_CELL_SIGNALING_IN_HELICOBACTER_PYLORI_INFECTION	-1.550103	-0.5914938	0.0045306	0.0490367	0.4070179	34	TCIRG1/ATP6V1G1/ATP6V1C1/ATP6V0A1/MAPK14/…
KEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION	-1.634120	-0.6729056	0.0027635	0.0317803	0.4317077	20	S1PR4/HRH2/NR3C1/CYSLTR1/CHRNB1/…
KEGG_SPLICEOSOME	-1.485363	-0.4918235	0.0017076	0.0209465	0.4550599	110	SNW1/CWC15/EIF4A3/TXNL4A/TRA2A/…
KEGG_PROTEASOME	-1.626411	-0.6076102	0.0011233	0.0160614	0.4550599	38	PSMB5/PSMB8/PSMA5/PSMA3/PSME1/…

11.1.3.7 TFT_GTRD

11.1.3.7.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.7.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.7.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.1.3.7.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HSD17B8_TARGET_GENES	2.505922	0.7051095	0.0000000	0.0000000	1.6595653	446	TK1/MKI67/UBE2C/CDC20/BIRC5/…
PSMB5_TARGET_GENES	2.085074	0.6145990	0.0000000	0.0000000	0.8513391	228	STMN1/LGALS1/RRM2/TUBA1B/H2AZ1/…
E2F5_TARGET_GENES	1.664771	0.4567061	0.0000000	0.0000000	0.8390889	871	TK1/STMN1/MKI67/BIRC5/TOP2A/…
AEBP2_TARGET_GENES	1.457557	0.4007301	0.0000198	0.0019530	0.5756103	802	TYMS/TK1/UBE2C/CENPM/MCM4/…
BARX1_TARGET_GENES	1.406124	0.3847913	0.0000227	0.0019530	0.5756103	973	UBE2C/CDC20/AREG/TOP2A/CENPF/…
SETD7_TARGET_GENES	1.471061	0.4063160	0.0000316	0.0023282	0.5573322	705	TYMS/STMN1/EIF5A/RRM2/DUT/…
ASH1L_TARGET_GENES	1.366928	0.3731608	0.0000421	0.0027179	0.5573322	1104	TK1/CENPM/TOP2A/CENPF/TPX2/…
ZNF597_TARGET_GENES	1.413176	0.3928013	0.0004363	0.0250162	0.4984931	589	LGALS1/CENPU/MCM7/H4C3/NFKBIA/…
PHF21A_TARGET_GENES	1.583297	0.4778271	0.0012691	0.0654868	0.4550599	178	TK1/RRM2/UHRF1/CDCA7/PTGDS/…
CSHL1_TARGET_GENES	-1.861877	-0.4777497	0.0000020	0.0002574	0.6272567	155	DERA/CCDC107/HM13/APEX1/ZNF19/…

11.1.3.7.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GTF2E2_TARGET_GENES	1.483473	0.3719489	0.0009853	0.0792437	0.4550599	285	KLF6/DUSP1/PPP4C/SNHG5/PPP1R15A/…
GTF2A2_TARGET_GENES	1.450753	0.3571803	0.0010750	0.0792437	0.4550599	349	ARPC5L/KLF6/DUSP1/JUND/MYC/…
ELF2_TARGET_GENES	-1.226398	-0.2632236	0.0009948	0.0792437	0.4550599	1200	C12orf4/DDX5/ACO2/SOCS4/LSM8/…
ZNF250_TARGET_GENES	-1.391894	-0.3235804	0.0009795	0.0792437	0.4550599	387	NDUFS3/DNAJC11/LSM8/STX18/NSL1/…
BARX2_TARGET_GENES	-1.233101	-0.2642737	0.0003832	0.0659146	0.4984931	1367	NDUFS3/C12orf4/LIN7C/SLC25A35/RPL41/…
ZSCAN2_TARGET_GENES	-1.380313	-0.3098029	0.0002463	0.0659146	0.4984931	573	C12orf4/MRPL45/POLR2K/MBD5/DDX31/…
ZZZ3_TARGET_GENES	-1.727348	-0.4849696	0.0003013	0.0659146	0.4984931	89	RPL41/CSNK1D/ZNF655/PSMD1/RPL37A/…

11.1.3.7.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
PSMB5_TARGET_GENES	1.914131	0.5194175	0.0000000	0.0000161	0.7195128	220	SGK1/SNHG5/NFKBIZ/ZFP36/LDHA/…
HMCES_TARGET_GENES	1.890097	0.8917071	0.0000747	0.0192655	0.5384341	9	MT-ND3/MT-CYB/MT-ND4/MT-ND4L/MT-ND6/…
SS18_SSX1_FUSION_UNIPROT_Q8IZH1_UNREVIEWED_TARGET_GENES	1.727403	0.5490251	0.0014488	0.0838067	0.4550599	61	PTGER2/ARID5B/LTBP3/RHOQ/KLF11/…
GTF2A2_TARGET_GENES	1.465602	0.3793365	0.0006167	0.0838067	0.4772708	345	SGK1/SNHG5/FOS/ZFP36/LDHA/…
ZNF282_TARGET_GENES	1.342630	0.3323978	0.0011904	0.0838067	0.4550599	690	PTGER2/SOCS3/IMPDH1/BAX/GUK1/…
ZNF560_TARGET_GENES	1.775012	0.6601199	0.0020491	0.0881121	0.4317077	27	HMGA1/LINC-PINT/OLFM2/CAPN2/KAT8/…
DROSHA_TARGET_GENES	1.736157	0.6032446	0.0017089	0.0881121	0.4550599	37	MT-ND3/MT-ND1/MT-ATP6/MT-ND2/EGR1/…
RARB_TARGET_GENES	1.559662	0.9743756	0.0019330	0.0881121	0.4550599	3	MT-ND3/MT-ND4/MT-ND4L
TFAM_TARGET_GENES	1.696094	0.8910341	0.0024859	0.0986694	0.4317077	6	MT-CYB/MT-ND2/MT-ND6/MT-ND5
NKX2_5_TARGET_GENES	-1.249709	-0.2684947	0.0006981	0.0838067	0.4772708	795	HMGCR/DHX8/APPBP2-DT/NRL/STARD4/…
DLX6_TARGET_GENES	-1.293392	-0.2890692	0.0013633	0.0838067	0.4550599	428	HMGCR/LINC02453/ABHD11/DHFR/ALKBH1/…
CDC5L_TARGET_GENES	-1.529188	-0.3793973	0.0009913	0.0838067	0.4550599	164	HMGCR/NDUFS3/ERAP1/ZNF207/ANKMY1/…
ZNF707_TARGET_GENES	-1.829861	-0.6452660	0.0014617	0.0838067	0.4550599	25	BRD1/PRMT5-AS1/COMMD10/HSPA4/ZNF48/…

11.1.3.7.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
PSMB5_TARGET_GENES	2.049872	0.5880093	0.0000000	0.0000108	0.7337620	222	ID1/ADM/SNHG5/H1-2/LDHA/…
ZNF407_TARGET_GENES	-1.228450	-0.2708254	0.0004416	0.0568529	0.4984931	1409	MYO1C/FTO/DNAJC1/QSOX1/VPS33A/…
SUPT20H_TARGET_GENES	-1.342613	-0.3036060	0.0000066	0.0011374	0.6105269	1033	VPS33A/EIF4A1/TTC31/MIS18A/RPS29/…
DIDO1_TARGET_GENES	-1.400445	-0.3117774	0.0000001	0.0000367	0.6901325	1247	AK9/NME7/DNAJC1/TTC31/EMC2/…

11.1.3.7.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
ZNF711_TARGET_GENES	-1.276627	-0.3897236	3.44e-04	0.0885925	0.4984931	861	WWP2/ADAP2/NOL11/DIS3L/MPC2/…
SUPT20H_TARGET_GENES	-1.359407	-0.4163061	7.60e-06	0.0038997	0.6105269	667	ADAP2/CCNT1/ACO2/ITGAE/PRMT1/…

11.1.3.8 TFT_TFT_Legacy

11.1.3.8.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.8.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.1.3.8.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.1.3.8.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
E2F_Q4	2.235083	0.6796246	0.0000000	0.0000000	0.9545416	188	STMN1/MCM4/CLSPN/RRM2/PCNA/…
E2F_Q3	2.181514	0.6730975	0.0000000	0.0000000	0.8986712	164	STMN1/MCM4/PCNA/H2AZ1/CDC45/…
E2F1DP2_01	2.199273	0.6793092	0.0000000	0.0000000	0.8870750	156	STMN1/MCM4/CLSPN/RRM2/PCNA/…
E2F_Q6_01	2.158422	0.6613600	0.0000000	0.0000000	0.8753251	176	STMN1/EIF5A/MCM4/RRM2/PCNA/…
E2F4DP1_01	2.143873	0.6508513	0.0000000	0.0000000	0.8753251	190	STMN1/MCM4/CLSPN/RRM2/PCNA/…
E2F_Q6	2.143655	0.6516007	0.0000000	0.0000000	0.8753251	184	STMN1/MCM4/CLSPN/RRM2/PCNA/…
E2F1_Q6	2.118278	0.6430303	0.0000000	0.0000000	0.8513391	187	STMN1/MCM4/CLSPN/RRM2/PCNA/…
E2F4DP2_01	2.113440	0.6430380	0.0000000	0.0000000	0.8513391	182	STMN1/MCM4/CLSPN/RRM2/PCNA/…
E2F_03	2.098822	0.6403090	0.0000000	0.0000000	0.8266573	179	STMN1/EIF5A/MCM4/LGALS1/H2AZ1/…
E2F1DP1RB_01	2.127433	0.6617685	0.0000000	0.0000000	0.8140358	145	STMN1/CLSPN/RRM2/PCNA/H2AZ1/…
CCANNAGRKGGC_UNKNOWN	-2.004137	-0.6146698	0.0001317	0.0036526	0.5188481	49	RPS4X/SSH2/EXT2/YY1/RPRD1B/…

11.1.3.8.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
ATF3_Q6	1.818764	0.4968675	0.0000227	0.0138582	0.5756103	136	JUN/DUSP1/JUND/AREG/PPP1R15A/…
MEF2_03	1.778060	0.5003649	0.0001783	0.0543947	0.5188481	110	RHOB/JUN/S1PR1/STRADB/FOS/…
TATAAA_TATA_01	1.426909	0.3404044	0.0003982	0.0809728	0.4984931	497	CD69/ZBTB10/EMP3/TSC22D3/MYC/…
MEF2_Q6_01	1.755602	0.5037722	0.0006396	0.0975350	0.4772708	98	RHOB/JUN/STRADB/JCHAIN/CD180/…

11.1.3.8.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
RACCACAR_AML_Q6	1.753159	0.4949765	0.0000882	0.0538283	0.5384341	140	EIF5A/CD69/ID3/RGS1/POLG2/…
AML_Q6	1.724139	0.4872107	0.0002042	0.0622800	0.5188481	136	EIF5A/CD69/RGS1/POLG2/SRSF2/…
STAT5B_01	1.701975	0.4870626	0.0005406	0.0715985	0.4772708	122	NFKBIA/PRDM1/SOCS2/CLDN5/OSM/…
CREL_01	1.680906	0.4729864	0.0004008	0.0715985	0.4984931	143	EIF5A/CD69/NFKBIA/CLDN5/FLOT1/…
HEN1_02	1.658047	0.4810706	0.0010251	0.0715985	0.4550599	105	LRRN3/IMPDH1/UBE2D3/LZTS2/F11R/…
NFKAPPAB65_01	1.657037	0.4736872	0.0008188	0.0715985	0.4772708	127	EIF5A/CD69/NFKBIA/CLDN5/FLOT1/…
AP1_Q4_01	1.646911	0.4653875	0.0009130	0.0715985	0.4772708	136	PRDM1/SRSF2/IL10/NR1D1/REXO2/…
CHOP_01	1.644826	0.4692138	0.0010564	0.0715985	0.4550599	128	NFKBIA/PRDM1/SRSF2/H1-4/KLF12/…
WGTTNNNNNAAA_UNKNOWN	1.494007	0.3912205	0.0008268	0.0715985	0.4772708	270	CD69/PRDM1/SOCS2/FLOT1/FOS/…
HEN1_01	1.690153	0.5090659	0.0015199	0.0927120	0.4550599	85	CLDN5/IMPDH1/UBE2D3/CDK6/LZTS2/…

11.1.3.8.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
LMO2COM_02	1.839400	0.5834059	0.0000978	0.0596486	0.5384341	102	CLU/ADM/ARHGEF10L/ID2/NAA38/…
PAX5_02	1.936611	0.8885264	0.0002801	0.0709771	0.4984931	12	PIM1/RASGRP2/PFN1
TGANTCA_AP1_C	1.488211	0.4006211	0.0003491	0.0709771	0.4984931	555	PIM1/ADM/FAM20A/LGALS1/FKBP5/…

11.2 Covid_Mild_vs_Healthy

11.2.1 Number of significant DEGs across conditions in each cluster

Here we check number of differentially expressed genes (DEGs) after applying some statistical thresholds:

Adjusted P value < 0.1
Log Fold Change > +/- 0
Proportion of cells expressing gene in condition A > 0.1 if LFC is positive
Proportion of cells expressing gene in condition B > 0.1 if LFC is negative

Differential expression compares genes in condition A (left) versus condition B (right). The left column indicates the number of genes upregulated in the left condition, while the right column indicates genes upregulated in the right condition.

Finally, the thresholds above do not affect downstream results from pathway analysis, they are just meant to count the number of DEGs. Downstream usage of DEGs can use these thresholds, or you can choose other appropriate cutoffs.

Cluster	Covid_Mild_high	Healthy_high
cluster_1	4	2
cluster_2	10	6
cluster_3	12	12
cluster_4	32	4
cluster_5	23	2
cluster_6	14	1
cluster_7	0	0

11.2.2 Per-cluster heatmaps of all DEGs

To ensure the differential expression results are robust, it is helpful to inspect all DEGs by visualizing them in a heatmap. For each cluster, we compare the DEGs at both the single cell level, and either the pseudobulk level (if we used pseudobulk_edgeR) or the average RISC value level (if using wilcox).

11.2.2.1 Cluster_1

11.2.2.1.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.2.2.1.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.2.2.2 Cluster_2

11.2.2.2.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.2.2.2.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.2.2.3 Cluster_3

11.2.2.3.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.2.2.3.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.2.2.4 Cluster_4

11.2.2.4.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.2.2.4.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.2.2.5 Cluster_5

11.2.2.5.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.2.2.5.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.2.2.6 Cluster_6

11.2.2.6.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.2.2.6.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.2.3 Gene set enrichment analysis across conditions

Pathway analysis for the cross-condition analysis is performed for overexpressed and underexpressed genes for each cluster. This is done via Gene Set Enrichment Analysis (GSEA) (Subramanian et al 2005).

GSEA is preferred over other pathway analysis such as fisher tests or chi-square tests because it does not require making arbitrary cutoffs to the number of DEGs and takes into account how strongly differentially expressed each gene may be. For the latter, data-driven gene-specific weight is applied. We use a standard weighting method of -log10(P-value) * sign of Log Fold Change.

The pathways we choose in pathway analysis are derived from the Molecular Signatures Database (MSIGDB) where they are sorted by categories, such as Gene Ontology (GO) Biological Process, GO Molecular Function, KEGG, Reactome, etc. These are databases that annotate genes by function or molecular pathway.

11.2.3.1 HALLMARK

11.2.3.1.1 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.1.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.1.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Mild and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Mild relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.2.3.1.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_INTERFERON_ALPHA_RESPONSE	1.860675	0.5225121	0.0034503	0.0575053	0.4317077	89	EIF2AK2/CMPK2/IFI44L/EPSTI1/OAS1/…
HALLMARK_INFLAMMATORY_RESPONSE	1.741909	0.4831213	0.0107553	0.0896279	0.3807304	104	NFKBIA/EIF2AK2/IL7R/PTGER4/CYBB/…
HALLMARK_INTERFERON_GAMMA_RESPONSE	1.670735	0.4333155	0.0095972	0.0896279	0.3807304	167	NFKBIA/EIF2AK2/IRF8/CMPK2/IFI44L/…
HALLMARK_DNA_REPAIR	-1.470810	-0.3853608	0.0069397	0.0867465	0.4070179	138	GMPR2/ERCC8/VPS37B/REV3L/NUDT21/…
HALLMARK_MYC_TARGETS_V1	-1.537450	-0.3866300	0.0014790	0.0415748	0.4550599	189	KPNB1/NCBP2/COX5A/PSMD14/SNRPA1/…
HALLMARK_OXIDATIVE_PHOSPHORYLATION	-1.537718	-0.3865418	0.0016630	0.0415748	0.4550599	190	COX11/COX7A2L/OGDH/ATP5MC3/COX5A/…

11.2.3.1.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_INTERFERON_GAMMA_RESPONSE	1.648761	0.4191647	0.0019	0.0950022	0.4550599	162	CD69/WARS1/IFI44L/SSPN/PFKP/…

11.2.3.1.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_INTERFERON_GAMMA_RESPONSE	1.836235	0.4509373	0.0000282	0.0014096	0.5756103	159	CD69/ARID5B/EIF2AK2/PDE4B/TNFAIP3/…
HALLMARK_INTERFERON_ALPHA_RESPONSE	1.779867	0.4827466	0.0005735	0.0095586	0.4772708	86	EIF2AK2/IFITM3/OASL/LY6E/MX1/…
HALLMARK_INFLAMMATORY_RESPONSE	1.618972	0.4276528	0.0039414	0.0394139	0.4070179	97	CD69/CCR7/EIF2AK2/PDE4B/PTGER2/…
HALLMARK_OXIDATIVE_PHOSPHORYLATION	-1.483782	-0.3894638	0.0037731	0.0394139	0.4317077	188	MRPL35/SDHB/UQCR11/MTRR/ATP5F1B/…
HALLMARK_MYC_TARGETS_V1	-1.651533	-0.4341729	0.0001322	0.0033056	0.5188481	186	NCBP2/CCT2/RAD23B/CAD/CCT4/…

11.2.3.1.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_INTERFERON_GAMMA_RESPONSE	2.219077	0.5766389	0.0000000	0.0000002	0.7614608	171	FCGR1A/IFI27/IFITM2/NFKBIA/MT2A/…
HALLMARK_INTERFERON_ALPHA_RESPONSE	2.024761	0.5815479	0.0000192	0.0003208	0.5756103	89	IFI27/IFITM2/IFITM3/IFITM1/OASL/…
HALLMARK_TNFA_SIGNALING_VIA_NFKB	1.949830	0.5144732	0.0000155	0.0003208	0.5756103	147	MAP3K8/GADD45B/ID2/BCL2A1/NFKBIA/…
HALLMARK_COMPLEMENT	1.842105	0.4912575	0.0000766	0.0009571	0.5384341	136	C1QC/CLU/C1QA/PIM1/GZMA/…
HALLMARK_KRAS_SIGNALING_DN	1.882308	0.6238281	0.0006570	0.0065702	0.4772708	40	IFI44L/RSAD2/CAMK1D/SLC16A7/LFNG/…
HALLMARK_INFLAMMATORY_RESPONSE	1.781600	0.4835323	0.0008720	0.0072671	0.4772708	122	NFKBIA/ADM/IFITM1/ABCA1/CD40/…
HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION	1.772822	0.5401254	0.0012207	0.0076297	0.4550599	63	GADD45B/ID2/SGCB/CXCL8/BMP1/…
HALLMARK_MYOGENESIS	1.682561	0.4971966	0.0025593	0.0127963	0.4317077	73	CLU/GADD45B/TNNT1/BHLHE40/CDKN1A/…
HALLMARK_COAGULATION	1.705623	0.5436959	0.0030711	0.0139595	0.4317077	51	CLU/C1QA/SERPING1/PF4/PRSS23/…
HALLMARK_TGF_BETA_SIGNALING	1.701278	0.5606248	0.0037771	0.0157378	0.4317077	41	ID1/ID2/SMAD7/KLF10/FNTA
HALLMARK_OXIDATIVE_PHOSPHORYLATION	-1.438241	-0.3508565	0.0054276	0.0208754	0.4070179	192	AIFM1/DLST/CYC1/TIMM50/NDUFB7/…
HALLMARK_DNA_REPAIR	-1.598664	-0.4080443	0.0018895	0.0104973	0.4550599	135	AAAS/HPRT1/USP11/TSG101/ITPA/…

11.2.3.1.3.5 cluster_5

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_INTERFERON_GAMMA_RESPONSE	2.460772	0.6327149	0	0	0.8870750	150	IFI27/IFI44L/IFIT1/ISG15/MX1/…
HALLMARK_INTERFERON_ALPHA_RESPONSE	2.618870	0.7359381	0	0	0.8390889	81	IFI27/IFI44L/IFITM1/ISG15/MX1/…

11.2.3.1.3.6 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_INTERFERON_ALPHA_RESPONSE	2.640576	0.7428698	0e+00	0.0e+00	0.8513391	74	IFI27/IFITM1/IFITM3/LY6E/MX1/…
HALLMARK_INTERFERON_GAMMA_RESPONSE	2.261363	0.5835799	1e-07	1.6e-06	0.7049757	137	IFI27/IFITM3/XAF1/LY6E/MX1/…

11.2.3.2 GO_BP

11.2.3.2.1 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.2.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.2.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Mild and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Mild relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.2.3.2.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_ADAPTIVE_IMMUNE_RESPONSE	2.369033	0.5602643	0.0000000	0.0000000	1.1512205	415	TRAV38-2DV8/TRAV8-4/TRAV26-2/TRAV12-2/TRBV20-1/…
GOBP_IMMUNE_RESPONSE	1.871305	0.4080207	0.0000000	0.0000000	1.0672100	1098	TRAV38-2DV8/TRAV8-4/TRAV26-2/FASLG/NFKBIA/…
GOBP_BIOLOGICAL_PROCESS_INVOLVED_IN_INTERSPECIES_INTERACTION_BETWEEN_ORGANISMS	1.566107	0.3463131	0.0000004	0.0006192	0.6749629	908	TRAV26-2/FASLG/NFKBIA/KIR3DL1/TRAV4/…
GOBP_RESPONSE_TO_BACTERIUM	1.865006	0.4481722	0.0000011	0.0014159	0.6435518	343	TRAV26-2/FASLG/NFKBIA/IGHV3-74/IGHV3-73/…
GOBP_DEFENSE_RESPONSE	1.481718	0.3278106	0.0000349	0.0290092	0.5573322	873	FASLG/NFKBIA/KIR3DL1/IGHV3-74/EIF2AK2/…
GOBP_DEFENSE_RESPONSE_TO_OTHER_ORGANISM	1.532996	0.3488651	0.0000419	0.0313079	0.5573322	634	FASLG/KIR3DL1/IGHV3-74/EIF2AK2/IGHV3-73/…
GOBP_INNATE_IMMUNE_RESPONSE	1.552066	0.3585867	0.0001527	0.0877615	0.5188481	516	FASLG/KIR3DL1/IGHV3-74/EIF2AK2/IGHV3-73/…
GOBP_CELLULAR_AMIDE_METABOLIC_PROCESS	-1.403867	-0.3110330	0.0000874	0.0543959	0.5384341	809	RPL13/MARS2/BTG2/METTL3/NEMF/…
GOBP_CELLULAR_MACROMOLECULE_BIOSYNTHETIC_PROCESS	-1.345345	-0.2955738	0.0000728	0.0494436	0.5384341	1030	RPL13/BTG2/METTL3/NEMF/B4GALT4/…
GOBP_AMIDE_BIOSYNTHETIC_PROCESS	-1.536444	-0.3463365	0.0000066	0.0061439	0.6105269	637	RPL13/MARS2/BTG2/METTL3/NEMF/…
GOBP_PEPTIDE_METABOLIC_PROCESS	-1.555445	-0.3506830	0.0000013	0.0014248	0.6435518	650	RPL13/MARS2/BTG2/METTL3/NEMF/…
GOBP_PEPTIDE_BIOSYNTHETIC_PROCESS	-1.637163	-0.3760544	0.0000004	0.0006192	0.6749629	523	RPL13/MARS2/BTG2/METTL3/NEMF/…
GOBP_CYTOPLASMIC_TRANSLATION	-2.097907	-0.5573015	0.0000000	0.0000570	0.7337620	134	RPL13/METTL3/NEMF/YTHDF2/RPL37A/…

11.2.3.2.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_ADAPTIVE_IMMUNE_RESPONSE	2.428342	0.5655280	0.0000000	0.0000000	1.1330899	362	IGHV5-10-1/IGKV1D-17/IGHV4-4/IGLV4-3/IGKV2-24/…
GOBP_IMMUNOGLOBULIN_PRODUCTION	2.349868	0.6032476	0.0000000	0.0000016	0.8012156	161	IGKV1D-17/IGLV4-3/IGKV2-24/IGLV5-45/IGLV5-37/…
GOBP_MEMBRANE_INVAGINATION	2.476607	0.6786930	0.0000000	0.0000023	0.7749390	101	IGHV5-10-1/IGHV4-4/IGHV3-20/IGHV1-24/IGHG2/…
GOBP_POSITIVE_REGULATION_OF_B_CELL_ACTIVATION	2.417318	0.6643847	0.0000000	0.0000023	0.7749390	105	IGHV5-10-1/IGHV4-4/IGHV3-20/IGHG2/IGHV1-69-2/…
GOBP_PHAGOCYTOSIS_RECOGNITION	2.542065	0.7466718	0.0000000	0.0000027	0.7614608	65	IGHV5-10-1/IGHV4-4/IGHV3-20/IGHV1-24/IGHG2/…
GOBP_COMPLEMENT_ACTIVATION	2.527229	0.7339930	0.0000000	0.0000034	0.7614608	70	IGHV5-10-1/IGHV4-4/IGHV3-20/IGHV1-24/IGHG2/…
GOBP_CELL_RECOGNITION	2.380411	0.6502955	0.0000000	0.0000034	0.7614608	108	IGHV5-10-1/IGHV4-4/IGHV3-20/IGHV1-24/IGHG2/…
GOBP_HUMORAL_IMMUNE_RESPONSE	2.346764	0.6201149	0.0000000	0.0000034	0.7614608	133	IGHV5-10-1/IGHV4-4/IGHV3-20/IGHV1-24/IGHG2/…
GOBP_B_CELL_RECEPTOR_SIGNALING_PATHWAY	2.388981	0.6525518	0.0000000	0.0000037	0.7477397	103	IGHV5-10-1/IGHV4-4/IGHV3-20/IGHV1-24/IGHG2/…
GOBP_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN	2.449157	0.7048141	0.0000000	0.0000097	0.7195128	74	IGHV5-10-1/IGHV4-4/IGHV3-20/IGHV1-24/IGHG2/…
GOBP_AMIDE_BIOSYNTHETIC_PROCESS	-1.376894	-0.3216217	0.0004876	0.0697338	0.4984931	633	NT5C3B/RPS14/CNOT8/BZW2/RBM4/…
GOBP_RIBOSOME_BIOGENESIS	-1.546860	-0.3983614	0.0004471	0.0651945	0.4984931	211	MRM1/RPS14/FTSJ3/POP5/RPS25/…
GOBP_PEPTIDE_BIOSYNTHETIC_PROCESS	-1.425108	-0.3374772	0.0004322	0.0642825	0.4984931	525	RPS14/CNOT8/BZW2/RBM4/PAIP2B/…
GOBP_NCRNA_PROCESSING	-1.528356	-0.3751245	0.0004294	0.0642825	0.4984931	323	MRM1/RPS14/FTSJ3/MRTO4/TRMT5/…
GOBP_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROCESS	-1.734358	-0.4821648	0.0003501	0.0553943	0.4984931	106	NT5C3B/CNOT8/MRTO4/NBDY/CNOT7/…
GOBP_NCRNA_METABOLIC_PROCESS	-1.449554	-0.3477894	0.0002790	0.0450951	0.4984931	435	MRM1/RPS14/FTSJ3/MRTO4/TRMT5/…
GOBP_RRNA_METABOLIC_PROCESS	-1.627560	-0.4115841	0.0001403	0.0237122	0.5188481	239	MRM1/RPS14/FTSJ3/MRTO4/KRI1/…
GOBP_PEPTIDE_METABOLIC_PROCESS	-1.431015	-0.3341687	0.0000556	0.0096124	0.5573322	650	NT5C3B/RPS14/CNOT8/BZW2/RBM4/…
GOBP_RNA_PROCESSING	-1.464733	-0.3379647	0.0000113	0.0020058	0.5933255	783	MRM1/RPS14/FTSJ3/RBM4/MRTO4/…
GOBP_CYTOPLASMIC_TRANSLATION	-1.925943	-0.5184987	0.0000023	0.0004238	0.6272567	136	RPS14/RBM4/RPL8/RPL7A/RPS25/…

11.2.3.2.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_ADAPTIVE_IMMUNE_RESPONSE	1.974708	0.4436898	0.0000000	0.0000001	0.8870750	416	TRBV18/TRBV5-5/TRBV10-1/TRAV27/TRBV6-6/…
GOBP_PROTEIN_DNA_COMPLEX_ASSEMBLY	2.110473	0.5362001	0.0000008	0.0019645	0.6594444	130	H1-4/H1-2/H3C2/H3C7/H3C3/…
GOBP_NUCLEOSOME_ASSEMBLY	2.168932	0.6008862	0.0000027	0.0049671	0.6272567	74	H1-4/H1-2/H3C2/H3C7/H3C3/…
GOBP_DNA_PACKAGING	1.901859	0.4674458	0.0000056	0.0083538	0.6105269	162	H1-4/H1-2/H3C2/H3C10/AKAP8L/…
GOBP_DEFENSE_RESPONSE_TO_BACTERIUM	1.889156	0.4692451	0.0000202	0.0187732	0.5756103	148	TRAV27/GSDMA/IGHV3-74/TNFRSF1A/IGKV3-20/…
GOBP_CHROMOSOME_CONDENSATION	2.216715	0.7133027	0.0000310	0.0242270	0.5573322	34	H1-4/H1-2/AKAP8L/H1-3/CDK1/…
GOBP_COMPLEMENT_ACTIVATION	2.112851	0.5985522	0.0000326	0.0242270	0.5573322	61	CLU/IGHV3-74/IGHV5-10-1/IGHV2-26/IGHV1-18/…
GOBP_REGULATION_OF_VIRAL_GENOME_REPLICATION	2.069228	0.5804109	0.0000477	0.0322515	0.5573322	67	EIF2AK2/IFITM3/OASL/MX1/OAS2/…
GOBP_NEGATIVE_REGULATION_OF_VIRAL_GENOME_REPLICATION	2.131175	0.6471560	0.0000584	0.0354966	0.5573322	45	EIF2AK2/IFITM3/OASL/MX1/OAS2/…
GOBP_PHAGOCYTOSIS_RECOGNITION	2.026473	0.5935845	0.0001426	0.0623784	0.5188481	52	IGHV3-74/IGHV5-10-1/IGHV2-26/IGHV1-18/IGHV3-73/…
GOBP_CYTOSOLIC_TRANSPORT	-1.736974	-0.4689094	0.0001922	0.0752298	0.5188481	133	USP7/TBC1D10C/DOP1A/ARFRP1/SNX5/…
GOBP_RIBOSOME_ASSEMBLY	-1.860932	-0.5904210	0.0001876	0.0752298	0.5188481	50	XRCC5/DDX28/SBDS/RPL10/MTERF3/…

11.2.3.2.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_COMPLEMENT_ACTIVATION	2.431221	0.7407868	0.0000000	0.0000135	0.7477397	58	C1QC/C1QB/CLU/C1QA/SERPING1/…
GOBP_NEGATIVE_REGULATION_OF_HEMOPOIESIS	2.362874	0.7243888	0.0000001	0.0001007	0.7049757	56	C1QC/ID2/SMAD7/RC3H2/BCL6/…
GOBP_CELL_JUNCTION_DISASSEMBLY	2.152596	0.9558911	0.0000001	0.0001012	0.6901325	10	C1QC/C1QB/C1QA
GOBP_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN	2.283553	0.6942718	0.0000008	0.0004842	0.6594444	59	C1QC/C1QB/CLU/C1QA/FCGR2B/…
GOBP_HUMORAL_IMMUNE_RESPONSE	2.104768	0.5693051	0.0000020	0.0008855	0.6272567	124	C1QC/C1QB/CLU/C1QA/RNASE2/…
GOBP_REGULATION_OF_VIRAL_GENOME_REPLICATION	2.257905	0.6685044	0.0000027	0.0010720	0.6272567	67	IFITM2/IFITM3/IFITM1/OASL/RSAD2/…
GOBP_NEGATIVE_REGULATION_OF_VIRAL_GENOME_REPLICATION	2.195126	0.7070095	0.0000117	0.0038146	0.5933255	44	IFITM2/IFITM3/IFITM1/OASL/APOBEC3A/…
GOBP_NEGATIVE_REGULATION_OF_VIRAL_PROCESS	2.093790	0.6222968	0.0000400	0.0093507	0.5573322	66	IFITM2/IFITM3/IFITM1/OASL/RSAD2/…
GOBP_RESPONSE_TO_TYPE_I_INTERFERON	2.113700	0.6548269	0.0001241	0.0238255	0.5188481	53	IFI27/IFITM2/IFITM3/IFITM1/ISG15/…
GOBP_RESPONSE_TO_INTERFERON_BETA	2.109431	0.7679136	0.0006536	0.0873725	0.4772708	24	IFITM2/IFITM3/IFITM1/AIM2/OAS1/…
GOBP_MRNA_CIS_SPLICING_VIA_SPLICEOSOME	-1.905173	-0.7051960	0.0007271	0.0938497	0.4772708	21	WBP11/WBP4/RBM17/CWC15/PRPF40A/…
GOBP_CELL_CYCLE_PROCESS	-1.318557	-0.2863453	0.0005959	0.0841725	0.4772708	731	CCNY/AAAS/PPP2R2D/RAD51B/SDCCAG8/…
GOBP_MRNA_METABOLIC_PROCESS	-1.354830	-0.2997693	0.0003729	0.0547292	0.4984931	566	WBP11/MOV10/DCP1B/TBRG4/RNPC3/…
GOBP_MRNA_PROCESSING	-1.428778	-0.3253080	0.0002580	0.0410893	0.4984931	402	WBP11/WBP4/TBRG4/RNPC3/NBDY/…
GOBP_VERY_LONG_CHAIN_FATTY_ACID_METABOLIC_PROCESS	-1.962502	-0.8056394	0.0001569	0.0279641	0.5188481	14	ABCD4/HACD2/ABCD3/TECR/HSD17B4
GOBP_NUCLEOSIDE_BISPHOSPHATE_METABOLIC_PROCESS	-1.822378	-0.5053500	0.0001314	0.0245887	0.5188481	79	DLST/HACD2/PDHA1/PAPSS1/TECR/…
GOBP_CELL_DIVISION	-1.527260	-0.3476624	0.0000303	0.0075616	0.5756103	378	CCNY/PPP2R2D/AHCTF1/NSMCE2/HAUS1/…

11.2.3.2.3.5 cluster_5

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_DEFENSE_RESPONSE_TO_SYMBIONT	2.141260	0.5455479	0.0000000	0.0000935	0.7477397	176	IFI27/APOBEC3A/IFIT1/IFITM1/IFI6/…
GOBP_RESPONSE_TO_VIRUS	1.930244	0.4736786	0.0000002	0.0007369	0.6901325	232	IFI27/IFI44L/APOBEC3A/IFIT1/IFITM1/…
GOBP_INNATE_IMMUNE_RESPONSE	1.697166	0.3933076	0.0000003	0.0007756	0.6749629	391	IFI27/C1QB/APOBEC3A/IFIT1/IFITM1/…
GOBP_RESPONSE_TO_TYPE_I_INTERFERON	2.519005	0.7725815	0.0000008	0.0014283	0.6594444	45	IFI27/IFIT1/IFITM1/ISG15/MX1/…
GOBP_DEFENSE_RESPONSE_TO_OTHER_ORGANISM	1.548131	0.3589170	0.0000015	0.0022037	0.6435518	489	IFI27/C1QB/IFI44L/APOBEC3A/IFIT1/…
GOBP_VIRAL_GENOME_REPLICATION	2.268702	0.6151272	0.0000043	0.0052296	0.6105269	90	IFI27/APOBEC3A/IFIT1/IFITM1/ISG15/…
GOBP_VIRAL_LIFE_CYCLE	1.882133	0.4747575	0.0000459	0.0371390	0.5573322	179	IFI27/APOBEC3A/IFIT1/IFITM1/ISG15/…
GOBP_NEGATIVE_REGULATION_OF_VIRAL_PROCESS	2.200760	0.6426592	0.0000885	0.0586393	0.5384341	61	APOBEC3A/IFIT1/IFITM1/ISG15/MX1/…
GOBP_NEGATIVE_REGULATION_OF_VIRAL_GENOME_REPLICATION	2.161283	0.6687871	0.0001194	0.0724794	0.5384341	42	APOBEC3A/IFIT1/IFITM1/ISG15/MX1/…
GOBP_SYNAPSE_PRUNING	1.983686	0.9596783	0.0001393	0.0780763	0.5188481	6	C1QB/C3

11.2.3.2.3.6 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_RESPONSE_TO_TYPE_I_INTERFERON	2.538164	0.7676580	0.0000001	0.0000930	0.7049757	46	IFI27/IFITM1/IFITM3/MX1/IFITM2/…
GOBP_DEFENSE_RESPONSE_TO_SYMBIONT	2.163648	0.5400973	0.0000009	0.0007951	0.6594444	163	IFI27/IFI6/IFITM1/IFITM3/DDIT4/…
GOBP_RESPONSE_TO_INTERFERON_BETA	2.442482	0.8649229	0.0000011	0.0008919	0.6435518	21	IFITM1/IFITM3/XAF1/IFITM2/CAPN2
GOBP_VIRAL_GENOME_REPLICATION	2.330376	0.6357810	0.0000012	0.0008919	0.6435518	87	IFI27/IFITM1/IFITM3/MX1/ISG20/…
GOBP_NEGATIVE_REGULATION_OF_VIRAL_ENTRY_INTO_HOST_CELL	2.160108	0.9026435	0.0000080	0.0052642	0.5933255	11	IFITM1/IFITM3/LY6E/IFITM2/FCN1/…
GOBP_NEGATIVE_REGULATION_OF_VIRAL_GENOME_REPLICATION	2.319381	0.7187952	0.0000817	0.0455892	0.5384341	38	IFITM1/IFITM3/MX1/ISG20/IFITM2/…
GOBP_MODULATION_BY_HOST_OF_VIRAL_GENOME_REPLICATION	2.141035	0.8545904	0.0000958	0.0496338	0.5384341	13	IFI27/STOM
GOBP_NEGATIVE_REGULATION_OF_VIRAL_LIFE_CYCLE	2.259694	0.8916490	0.0001154	0.0558291	0.5384341	14	IFITM1/IFITM3/LY6E/IFITM2/FCN1/…
GOBP_NEGATIVE_REGULATION_OF_VIRAL_PROCESS	2.341837	0.6833816	0.0001648	0.0612277	0.5188481	55	IFITM1/IFITM3/LY6E/MX1/ISG20/…
GOBP_REGULATION_OF_BIOLOGICAL_PROCESS_INVOLVED_IN_SYMBIOTIC_INTERACTION	2.324339	0.7864799	0.0003155	0.0995452	0.4984931	26	IFITM1/IFITM3/LY6E/IFITM2/FCN1/…

11.2.3.3 GO_MF

11.2.3.3.1 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.3.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.3.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Mild and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Mild relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.2.3.3.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_ANTIGEN_BINDING	2.117260	0.5683886	8.39e-05	0.0351880	0.5384341	123	TRAV8-4/TRAV12-2/IGHV3-74/IGHV3-73/IL7R/…
GOMF_SIGNALING_RECEPTOR_BINDING	1.541810	0.3528771	7.53e-05	0.0351880	0.5384341	601	S100B/TRAV8-4/FASLG/IGHV3-74/IGHV3-73/…
GOMF_LIGASE_ACTIVITY	-1.808788	-0.4785819	6.30e-05	0.0351880	0.5384341	132	PCCA/MARS2/NADSYN1/SAE1/AARS1/…
GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME	-1.892482	-0.4919282	4.50e-06	0.0076226	0.6105269	148	RPL13/MRPL41/RPL37A/RPL10A/RPL4/…

11.2.3.3.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_ANTIGEN_BINDING	2.426503	0.6422670	0.00e+00	0.0000011	0.8012156	126	IGHV5-10-1/IGHV4-4/IGHV3-20/IGHV1-24/IGHG2/…
GOMF_IMMUNOGLOBULIN_RECEPTOR_BINDING	2.523315	0.7445718	1.00e-07	0.0000583	0.7049757	61	IGHV5-10-1/IGHV4-4/IGHV3-20/IGHV1-24/IGHV1-69-2/…
GOMF_SIGNALING_RECEPTOR_BINDING	1.489865	0.3340615	2.67e-05	0.0088396	0.5756103	570	IGHV4-4/IGHV3-20/IGHV1-24/IGHG2/IGHV1-69-2/…
GOMF_RNA_BINDING	-1.362048	-0.3039544	2.28e-05	0.0088396	0.5756103	1389	MRM1/N4BP1/RPS14/CNOT8/FTSJ3/…
GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME	-1.902452	-0.5004558	2.60e-06	0.0014351	0.6272567	149	RPS14/RPL8/RPL7A/RPS25/MRPS35/…

11.2.3.3.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_ANTIGEN_BINDING	1.967821	0.5092179	9.5e-06	0.015686	0.5933255	119	IGLV3-21/IGKV2-30/IGHV3-74/IGKV3-20/HLA-DRB5/…

11.2.3.3.3.4 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_LAMIN_BINDING	2.070413	0.9126548	6.71e-05	0.057805	0.5384341	10	IFI27/SUN1
GOMF_ANTIGEN_BINDING	2.062157	0.6127616	7.26e-05	0.057805	0.5384341	55	IGLV1-40/IGHV1-69D/CD1C/IGLV3-21/IGKV3-20/…

11.2.3.4 GO_CC

11.2.3.4.1 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.4.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.4.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Mild and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Mild relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.2.3.4.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_T_CELL_RECEPTOR_COMPLEX	2.810769	0.8135368	0.0000000	0.0000000	1.0175448	79	TRAV38-2DV8/TRAV8-4/TRAV26-2/TRAV12-2/TRBV20-1/…
GOCC_PLASMA_MEMBRANE_SIGNALING_RECEPTOR_COMPLEX	2.480113	0.6597571	0.0000000	0.0000000	0.9101197	144	TRAV38-2DV8/TRAV8-4/TRAV26-2/TRAV12-2/TRBV20-1/…
GOCC_RECEPTOR_COMPLEX	2.372804	0.5944980	0.0000000	0.0000000	0.8986712	214	TRAV38-2DV8/TRAV8-4/TRAV26-2/TRAV12-2/TRBV20-1/…
GOCC_PLASMA_MEMBRANE_PROTEIN_COMPLEX	2.187653	0.5304449	0.0000000	0.0000000	0.8266573	280	TRAV38-2DV8/TRAV8-4/TRAV26-2/TRAV12-2/TRBV20-1/…
GOCC_SOMATODENDRITIC_COMPARTMENT	1.741551	0.4175717	0.0001192	0.0147172	0.5384341	315	S100B/CYBB/SLA/STAT1/NCDN/…
GOCC_MEMBRANE_PROTEIN_COMPLEX	1.436362	0.3236719	0.0002872	0.0236458	0.4984931	739	TRAV38-2DV8/TRAV8-4/TRAV26-2/TRAV12-2/TRBV20-1/…
GOCC_CELL_BODY	1.764825	0.4429838	0.0007927	0.0602450	0.4772708	212	S100B/CYBB/NCDN/TPX2/TOP1/…
GOCC_MHC_CLASS_II_PROTEIN_COMPLEX	1.834732	0.7393508	0.0013192	0.0930986	0.4550599	15	HLA-DQB2/HLA-DRB5/HLA-DOB/HLA-DRB1/HLA-DPA1/…
GOCC_CYTOSOLIC_RIBOSOME	-1.783070	-0.4942858	0.0002826	0.0236458	0.4984931	91	RPL37A/RPS27/RPL10A/RPL4/RPL27A/…
GOCC_INTRACELLULAR_PROTEIN_CONTAINING_COMPLEX	-1.425841	-0.3218254	0.0001487	0.0157025	0.5188481	595	PPP2R1B/PSMB10/CSNK2B/PPP4R2/FBXO8/…
GOCC_RIBOSOMAL_SUBUNIT	-1.632796	-0.4211812	0.0001589	0.0157025	0.5188481	161	RPL13/MRPL41/RPL37A/RPS27/RPL10A/…
GOCC_CATALYTIC_COMPLEX	-1.335781	-0.2921304	0.0000979	0.0138217	0.5384341	1165	GMPR2/PPP2R1B/RCOR3/POP7/PSMB10/…
GOCC_LARGE_RIBOSOMAL_SUBUNIT	-1.880783	-0.5119930	0.0000398	0.0065560	0.5573322	103	RPL13/MRPL41/RPL10A/RPL4/MRPL55/…
GOCC_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT	-2.227967	-0.6842072	0.0000005	0.0000913	0.6749629	50	RPL13/RPL37A/RPL10A/RPL4/RPL27A/…

11.2.3.4.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_IMMUNOGLOBULIN_COMPLEX	2.877338	0.7668488	0.0000000	0.0000000	1.1512205	124	IGHV5-10-1/IGKV1D-17/IGHV4-4/IGLV4-3/IGKV2-24/…
GOCC_IMMUNOGLOBULIN_COMPLEX_CIRCULATING	2.501185	0.7606068	0.0000000	0.0000002	0.8140358	60	IGHV5-10-1/IGHV4-4/IGHV3-20/IGHV1-24/IGHG2/…
GOCC_EXTERNAL_SIDE_OF_PLASMA_MEMBRANE	2.120924	0.5267277	0.0000000	0.0000056	0.7337620	209	IGHV5-10-1/IGHV4-4/CD69/IGHV3-20/IGHV1-24/…
GOCC_CELL_SURFACE	1.841232	0.4294251	0.0000002	0.0000448	0.6901325	355	IGHV5-10-1/IGHV4-4/CD69/IGHV3-20/IGHV1-24/…
GOCC_SIDE_OF_MEMBRANE	1.819743	0.4293807	0.0000008	0.0001334	0.6594444	333	IGHV5-10-1/IGHV4-4/IGHV3-20/IGHV1-24/IGHG2/…
GOCC_CYTOCHROME_COMPLEX	-1.875807	-0.6551574	0.0003830	0.0189385	0.4984931	29	COX6C/COX7B/NDUFA4/UQCR11/COX7C/…
GOCC_RESPIRATORY_CHAIN_COMPLEX_IV	-1.910721	-0.7750233	0.0002386	0.0124183	0.5188481	15	COX6C/COX7B/NDUFA4/COX7C/COX5A/…
GOCC_CYTOSOLIC_SMALL_RIBOSOMAL_SUBUNIT	-1.883362	-0.6242331	0.0001859	0.0102117	0.5188481	39	RPS14/RPS25/RPS17/RPS28/RPS27A/…
GOCC_POLYSOMAL_RIBOSOME	-1.985078	-0.6933221	0.0000647	0.0045713	0.5384341	29	RPL8/RPL7A/RPL18A/RPS28/RPL39/…
GOCC_LARGE_RIBOSOMAL_SUBUNIT	-1.843497	-0.5156260	0.0000243	0.0019993	0.5756103	103	RPL8/RPL7A/MRPL52/RPL35A/MRPL44/…
GOCC_RESPIRASOME	-1.921574	-0.5485906	0.0000181	0.0017926	0.5756103	89	COX6C/COX7B/NDUFA4/UQCR11/HIGD2A/…
GOCC_POLYSOME	-2.060628	-0.6289142	0.0000071	0.0007761	0.6105269	58	MCRS1/RPL8/RPL7A/AGO1/RPL18A/…
GOCC_RIBOSOMAL_SUBUNIT	-1.837359	-0.4836669	0.0000060	0.0007453	0.6105269	160	RPS14/RPL8/RPL7A/RPS25/MRPS28/…
GOCC_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT	-2.091839	-0.6563991	0.0000018	0.0002555	0.6435518	50	RPL8/RPL7A/RPL35A/RPL18A/RPL14/…
GOCC_CYTOSOLIC_RIBOSOME	-2.145407	-0.6109694	0.0000000	0.0000056	0.7337620	91	RPS14/RPL8/RPL7A/RPS25/RPL35A/…

11.2.3.4.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_IMMUNOGLOBULIN_COMPLEX	2.160772	0.5807815	0.0000011	0.0009178	0.6435518	92	IGLV3-21/IGKV2-30/IGHV3-74/IGKV3-20/IGLV9-49/…
GOCC_T_CELL_RECEPTOR_COMPLEX	2.082930	0.5608558	0.0000019	0.0009178	0.6435518	95	TRBV18/TRBV5-5/TRBV10-1/TRBV6-6/TRAV14DV4/…
GOCC_DNA_PACKAGING_COMPLEX	2.092896	0.5757801	0.0000074	0.0024103	0.6105269	80	H1-4/H1-2/H3C2/H3C10/H3C7/…
GOCC_PROTEIN_DNA_COMPLEX	1.858029	0.4773662	0.0000101	0.0024698	0.5933255	135	H1-4/H1-2/H3C2/H3C10/H3C7/…
GOCC_PLASMA_MEMBRANE_PROTEIN_COMPLEX	1.647734	0.3837028	0.0000323	0.0052788	0.5573322	283	TRBV18/TRBV5-5/TRBV10-1/TRAV27/TRBV6-6/…
GOCC_SPHERICAL_HIGH_DENSITY_LIPOPROTEIN_PARTICLE	1.624244	0.9913244	0.0000275	0.0052788	0.5756103	3	CLU/APOM
GOCC_PLASMA_MEMBRANE_SIGNALING_RECEPTOR_COMPLEX	1.820071	0.4537437	0.0000505	0.0070777	0.5573322	154	TRBV18/TRBV5-5/TRBV10-1/TRAV27/TRBV6-6/…
GOCC_RECEPTOR_COMPLEX	1.587443	0.3831787	0.0001581	0.0193832	0.5188481	213	TRBV18/TRBV5-5/TRBV10-1/TRAV27/TRBV6-6/…
GOCC_IMMUNOGLOBULIN_COMPLEX_CIRCULATING	1.939151	0.5819781	0.0002973	0.0291684	0.4984931	48	IGHV3-74/IGKV3-20/IGHV5-10-1/IGHV2-26/IGHV1-18/…
GOCC_CALCIUM_CHANNEL_COMPLEX	2.013736	0.7540111	0.0005838	0.0520672	0.4772708	17	PDE4D/PDE4B/MCUB/MICU1/PTPA/…
GOCC_AUTOPHAGOSOME_MEMBRANE	-1.785206	-0.5962987	0.0009338	0.0763369	0.4772708	39	ATP6AP2/ATG9A/RAB7A/TECPR1/ATG14/…
GOCC_MITOCHONDRIAL_PROTEIN_CONTAINING_COMPLEX	-1.574719	-0.3988374	0.0002286	0.0249173	0.5188481	250	MRPL35/SDHB/IMMP1L/UQCR11/MRPL38/…

11.2.3.4.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_BLOOD_MICROPARTICLE	2.293026	0.7360336	0.0000003	0.0001473	0.6749629	52	C1QC/C1QB/CLU/IGLV3-21/HSPA1B/…
GOCC_EXTERNAL_ENCAPSULATING_STRUCTURE	2.122080	0.5969957	0.0000003	0.0001473	0.6749629	109	C1QC/C1QB/CLU/C1QA/SERPING1/…
GOCC_COLLAGEN_TRIMER	2.059335	0.9498680	0.0000009	0.0002230	0.6594444	10	C1QC/C1QB/MARCO/C1QA/COL8A2
GOCC_COLLAGEN_CONTAINING_EXTRACELLULAR_MATRIX	2.113216	0.6098160	0.0000029	0.0005685	0.6272567	89	C1QC/C1QB/C1QA/SERPING1/LGALS3BP/…
GOCC_HIGH_DENSITY_LIPOPROTEIN_PARTICLE	1.911153	0.9667868	0.0000337	0.0055450	0.5573322	7	CLU/LCAT/PLA2G7
GOCC_CELL_SURFACE	1.570645	0.3805374	0.0000698	0.0098402	0.5384341	362	CLU/ABCA1/CD40/FCGR2B/FOLR3/…
GOCC_IMMUNOGLOBULIN_COMPLEX	1.775126	0.5226512	0.0004965	0.0535858	0.4772708	83	IGLV3-21/IGLV3-1/TRDC/IGLV3-19/IGKV2D-30/…
GOCC_DNA_POLYMERASE_COMPLEX	-1.827871	-0.7049446	0.0010862	0.0765785	0.4550599	18	MAD2L2/POLD2/POLG/POLD3/PRIM2/…
GOCC_INTRACELLULAR_PROTEIN_CONTAINING_COMPLEX	-1.331250	-0.2914627	0.0006548	0.0538577	0.4772708	576	PPP2R2D/SYVN1/PEF1/FBXW5/DERL2/…
GOCC_MITOCHONDRIAL_PROTEIN_CONTAINING_COMPLEX	-1.474939	-0.3492730	0.0005972	0.0535858	0.4772708	251	MFN1/TIMM22/CYC1/MRPL55/TIMM50/…
GOCC_THO_COMPLEX	-1.762032	-0.8997731	0.0005736	0.0535858	0.4772708	6	THOC1/THOC2/THOC6
GOCC_TRANSFERASE_COMPLEX	-1.350484	-0.2945855	0.0003318	0.0409368	0.4984931	609	CCNY/HCFC2/PIGM/SYVN1/DLST/…
GOCC_CATALYTIC_COMPLEX	-1.407214	-0.2954801	0.0000008	0.0002230	0.6594444	1120	CCNY/HCFC2/PPP2R2D/PIGM/DLST/…

11.2.3.4.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_UBIQUITIN_CONJUGATING_ENZYME_COMPLEX	-1.776950	-0.8913531	0.0001535	0.0738115	0.5188481	7	UBE2B/RNF20/UBE2N/UBE2A
GOCC_MITOCHONDRIAL_PROTEIN_CONTAINING_COMPLEX	-1.749688	-0.4445528	0.0000022	0.0020777	0.6272567	227	SDHB/CHCHD3/MRPS9/NDUFS4/NDUFB9/…

11.2.3.5 CP_REACTOME

11.2.3.5.1 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.5.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.5.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Mild and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Mild relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.2.3.5.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_NUCLEAR_SIGNALING_BY_ERBB4	2.237662	0.9034019	0.0000124	0.0016262	0.5933255	15	S100B/TAB2/ADAM17
REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM	1.515736	0.3530944	0.0003204	0.0297386	0.4984931	501	S100B/FASLG/NFKBIA/EIF2AK2/IFNGR1/…
REACTOME_SIGNALING_BY_ERBB4	2.250647	0.7763313	0.0005032	0.0417958	0.4772708	29	S100B/TAB2
REACTOME_DDX58_IFIH1_MEDIATED_INDUCTION_OF_INTERFERON_ALPHA_BETA	2.189480	0.6625634	0.0007737	0.0530800	0.4772708	59	S100B/NFKBIA/TNFAIP3/HERC5/IRF7/…
REACTOME_FASL_CD95L_SIGNALING	1.760740	0.9416871	0.0009368	0.0615960	0.4772708	5	FASLG/CASP10
REACTOME_TAK1_ACTIVATES_NFKB_BY_PHOSPHORYLATION_AND_ACTIVATION_OF_IKKS_COMPLEX	2.084233	0.7189291	0.0014383	0.0785566	0.4550599	29	S100B/NFKBIA/S100A12/NKIRAS2/TRAF6
REACTOME_ADVANCED_GLYCOSYLATION_ENDPRODUCT_RECEPTOR_SIGNALING	2.034035	0.8809841	0.0013110	0.0785566	0.4550599	11	S100B/LGALS3/S100A12
REACTOME_TRAF6_MEDIATED_NF_KB_ACTIVATION	2.216092	0.8013799	0.0016318	0.0804660	0.4550599	22	S100B/NFKBIA/S100A12/NKIRAS2/TRAF6
REACTOME_MITOTIC_TELOPHASE_CYTOKINESIS	1.853543	0.7906865	0.0016039	0.0804660	0.4550599	12	KIF23/PLK1/WAPL/PDS5B/PDS5A/…
REACTOME_IMMUNOREGULATORY_INTERACTIONS_BETWEEN_A_LYMPHOID_AND_A_NON_LYMPHOID_CELL	1.929572	0.5127398	0.0021142	0.0953214	0.4317077	130	TRAV8-4/KIR3DL1/IGLV3-21/IGKV4-1/IGLV3-1/…
REACTOME_ACTIVATION_OF_THE_MRNA_UPON_BINDING_OF_THE_CAP_BINDING_COMPLEX_AND_EIFS_AND_SUBSEQUENT_BINDING_TO_43S	-1.912835	-0.5771269	0.0004972	0.0417958	0.4772708	52	RPS27/EIF4EBP1/RPS2/RPS23/EIF4B/…
REACTOME_RESPONSE_OF_EIF2AK4_GCN2_TO_AMINO_ACID_DEFICIENCY	-1.967008	-0.5513496	0.0000034	0.0004815	0.6272567	99	RPL13/RPL37A/RPS27/RPL10A/RPL4/…
REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE	-1.987362	-0.5469227	0.0000017	0.0003003	0.6435518	107	RPL13/SEC61G/RPL37A/RPS27/RPL10A/…
REACTOME_SELENOAMINO_ACID_METABOLISM	-2.033909	-0.5688141	0.0000008	0.0001575	0.6594444	98	RPL13/RPL37A/RPL10A/RPL4/RPL27A/…
REACTOME_NONSENSE_MEDIATED_DECAY_NMD	-2.028055	-0.5551614	0.0000004	0.0001098	0.6749629	110	RPL13/RPL37A/RPS27/RPL10A/NCBP2/…
REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION	-2.105282	-0.5998711	0.0000004	0.0001098	0.6749629	86	RPL13/RPL37A/RPL10A/RPL4/RPL27A/…
REACTOME_EUKARYOTIC_TRANSLATION_INITIATION	-2.057748	-0.5802567	0.0000002	0.0000858	0.6901325	94	RPL13/RPL37A/RPS27/RPL10A/RPL4/…
REACTOME_METABOLISM_OF_AMINO_ACIDS_AND_DERIVATIVES	-1.859176	-0.4578624	0.0000002	0.0000827	0.6901325	252	RPL13/PSMB10/OGDH/RPL37A/PSMC3/…
REACTOME_REGULATION_OF_EXPRESSION_OF_SLITS_AND_ROBOS	-2.044104	-0.5317577	0.0000001	0.0000459	0.7195128	150	RPL13/PSMB10/RPL37A/PSMC3/RPS27/…
REACTOME_SIGNALING_BY_ROBO_RECEPTORS	-2.025743	-0.5175251	0.0000000	0.0000221	0.7477397	180	RPL13/PSMB10/VASP/RPL37A/PSMC3/…

11.2.3.5.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_CELL_SURFACE_INTERACTIONS_AT_THE_VASCULAR_WALL	1.931837	0.5203953	0.0001405	0.0121494	0.5188481	114	IGKV2D-30/IGHV2-70/JCHAIN/IGLV7-43/DOK2/…
REACTOME_ROLE_OF_PHOSPHOLIPIDS_IN_PHAGOCYTOSIS	1.966460	0.5749386	0.0002815	0.0219183	0.4984931	71	IGHG2/IGKV2D-30/IGHV2-70/IGLV7-43/IGKV2D-28/…
REACTOME_INITIAL_TRIGGERING_OF_COMPLEMENT	1.955028	0.5939960	0.0004891	0.0346166	0.4772708	55	IGHG2/IGKV2D-30/IGHV2-70/IGLV7-43/IGKV2D-28/…
REACTOME_ROLE_OF_LAT2_NTAL_LAB_ON_CALCIUM_MOBILIZATION	1.916228	0.5752834	0.0007124	0.0482238	0.4772708	60	IGKV2D-30/IGHV2-70/IGLV7-43/IGKV2D-28/IGKV1D-16/…
REACTOME_COMPLEMENT_CASCADE	1.947265	0.5812776	0.0008428	0.0529726	0.4772708	64	IGHG2/IGKV2D-30/IGHV2-70/IGLV7-43/IGKV2D-28/…
REACTOME_FCGR_ACTIVATION	1.871883	0.5619701	0.0011434	0.0642208	0.4550599	60	IGHG2/IGKV2D-30/IGHV2-70/IGLV7-43/IGKV2D-28/…
REACTOME_FCERI_MEDIATED_CA_2_MOBILIZATION	1.862655	0.5404683	0.0011961	0.0642208	0.4550599	75	IGKV2D-30/IGHV2-70/IGLV7-43/IGKV2D-28/IGKV1D-16/…
REACTOME_CREATION_OF_C4_AND_C2_ACTIVATORS	1.933300	0.5891708	0.0014435	0.0721250	0.4550599	53	IGHG2/IGKV2D-30/IGHV2-70/IGLV7-43/IGKV2D-28/…
REACTOME_SCAVENGING_OF_HEME_FROM_PLASMA	1.897282	0.5867983	0.0017384	0.0820200	0.4550599	49	IGKV2D-30/IGHV2-70/IGKV2D-28/IGHA1/IGKV1D-16/…
REACTOME_FCERI_MEDIATED_MAPK_ACTIVATION	1.806300	0.5281123	0.0020414	0.0908137	0.4317077	71	IGKV2D-30/JUN/IGHV2-70/IGLV7-43/IGKV2D-28/…
REACTOME_RESPIRATORY_ELECTRON_TRANSPORT	-1.884359	-0.5378053	0.0000102	0.0010639	0.5933255	96	COX16/COX6C/COX7B/NDUFA4/UQCR11/…
REACTOME_INFLUENZA_INFECTION	-1.864096	-0.5015747	0.0000031	0.0003989	0.6272567	150	RPS14/RPL8/RPL7A/POLR2I/RPS25/…
REACTOME_CELLULAR_RESPONSE_TO_STARVATION	-1.905033	-0.5187183	0.0000014	0.0002366	0.6435518	136	RPS14/RPL8/RPL7A/RPS25/RPL35A/…
REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE	-2.074744	-0.5865145	0.0000000	0.0000091	0.7195128	108	RPS14/RPL8/RPL7A/RPS25/RPL35A/…
REACTOME_EUKARYOTIC_TRANSLATION_INITIATION	-2.090081	-0.5983459	0.0000000	0.0000091	0.7195128	95	RPS14/RPL7A/RPS25/RPL35A/RPS17/…
REACTOME_REGULATION_OF_EXPRESSION_OF_SLITS_AND_ROBOS	-2.041356	-0.5502725	0.0000000	0.0000029	0.7477397	149	RPS14/RPL8/RPL7A/RPS25/PSMD5/…
REACTOME_SELENOAMINO_ACID_METABOLISM	-2.128612	-0.6085994	0.0000000	0.0000029	0.7477397	98	RPS14/RPL8/RPL7A/RPS25/RPL35A/…
REACTOME_NONSENSE_MEDIATED_DECAY_NMD	-2.149436	-0.6056000	0.0000000	0.0000021	0.7749390	111	RPS14/RPL8/RPL7A/SMG8/RPS25/…
REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION	-2.211749	-0.6367378	0.0000000	0.0000021	0.7614608	87	RPS14/RPL7A/RPS25/RPL35A/RPS17/…
REACTOME_RESPONSE_OF_EIF2AK4_GCN2_TO_AMINO_ACID_DEFICIENCY	-2.184671	-0.6246276	0.0000000	0.0000015	0.7881868	98	RPS14/RPL8/RPL7A/RPS25/RPL35A/…

11.2.3.5.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_INITIAL_TRIGGERING_OF_COMPLEMENT	2.127200	0.6453466	0.0000285	0.0148474	0.5756103	48	IGLV3-21/IGKV2-30/IGHV1-2/IGKV3-20/IGHV3-48/…
REACTOME_CREATION_OF_C4_AND_C2_ACTIVATORS	2.078025	0.6367109	0.0000274	0.0148474	0.5756103	45	IGLV3-21/IGKV2-30/IGHV1-2/IGKV3-20/IGHV3-48/…
REACTOME_COMPLEMENT_CASCADE	2.066804	0.5998780	0.0000640	0.0200087	0.5384341	57	IGLV3-21/IGKV2-30/CLU/IGHV1-2/IGKV3-20/…
REACTOME_CD22_MEDIATED_BCR_REGULATION	2.024266	0.6202391	0.0000586	0.0200087	0.5573322	45	IGLV3-21/IGKV2-30/IGHV1-2/IGKV3-20/IGHV3-48/…
REACTOME_HDMS_DEMETHYLATE_HISTONES	2.067104	0.6666766	0.0001782	0.0422035	0.5188481	36	H3C2/H3C10/ARID5B/H3C7/KDM5D/…
REACTOME_FORMATION_OF_SENESCENCE_ASSOCIATED_HETEROCHROMATIN_FOCI_SAHF	2.044491	0.8274335	0.0002370	0.0422035	0.5188481	13	H1-4/H1-2/H1-3/ASF1A/H1-5
REACTOME_INTERLEUKIN_7_SIGNALING	2.005942	0.7153777	0.0002543	0.0422035	0.4984931	21	H3C2/H3C10/H3C7/H3C3/H3C13/…
REACTOME_ROLE_OF_LAT2_NTAL_LAB_ON_CALCIUM_MOBILIZATION	1.955232	0.5872321	0.0003124	0.0422035	0.4984931	50	IGLV3-21/IGKV2-30/IGHV1-2/IGKV3-20/IGHV3-48/…
REACTOME_FCGR_ACTIVATION	1.950651	0.5858563	0.0003238	0.0422035	0.4984931	50	IGLV3-21/IGKV2-30/IGHV1-2/IGKV3-20/IGHV3-48/…
REACTOME_PEPTIDE_LIGAND_BINDING_RECEPTORS	1.960903	0.6488797	0.0008084	0.0602090	0.4772708	30	CCR7/CXCR4/CCL3L3/POMC/CCR5/…
REACTOME_RAB_GERANYLGERANYLATION	-1.732910	-0.6004349	0.0020598	0.0826044	0.4317077	33	RAB4B/RAB7A/RAB2B/RAB8A/RAB39B/…
REACTOME_METABOLISM_OF_ANGIOTENSINOGEN_TO_ANGIOTENSINS	-1.734302	-0.8540047	0.0017201	0.0747270	0.4550599	7	ATP6AP2/CTSZ/AOPEP/ACE/GZMH
REACTOME_METABOLISM_OF_AMINO_ACIDS_AND_DERIVATIVES	-1.487242	-0.3801159	0.0014347	0.0723806	0.4550599	245	OAZ1/MTR/MTRR/SARDH/SEM1/…
REACTOME_REGULATION_OF_EXPRESSION_OF_SLITS_AND_ROBOS	-1.586072	-0.4296856	0.0010839	0.0627842	0.4550599	147	NCBP2/SEM1/RPL10/RPL7A/RPL14/…
REACTOME_SIGNALING_BY_ROBO_RECEPTORS	-1.586987	-0.4205260	0.0008839	0.0627842	0.4772708	173	NCBP2/VASP/SEM1/RPL10/RPL7A/…
REACTOME_NUCLEOTIDE_EXCISION_REPAIR	-1.674600	-0.4730561	0.0010321	0.0627842	0.4550599	104	USP7/RFC2/RAD23B/RFC3/GTF2H2/…
REACTOME_ASSOCIATION_OF_TRIC_CCT_WITH_TARGET_PROTEINS_DURING_BIOSYNTHESIS	-1.826008	-0.6527378	0.0010579	0.0627842	0.4550599	28	LONP2/CCT2/CCT4/DCAF7/CCNE2/…
REACTOME_SIGNALING_BY_MET	-1.834942	-0.6195994	0.0007180	0.0563916	0.4772708	37	RAB4B/PTPN2/CBL/RANBP9/NRAS/…
REACTOME_TRANSLATION	-1.498196	-0.3771111	0.0005766	0.0548477	0.4772708	277	MRPL35/MRPL38/APEH/SSR1/MRPS16/…
REACTOME_NERVOUS_SYSTEM_DEVELOPMENT	-1.556695	-0.3854789	0.0002612	0.0422035	0.4984931	320	DLG1/NCBP2/VASP/APH1B/DLG3/…

11.2.3.5.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_COMPLEMENT_CASCADE	2.328440	0.7095176	0.0000001	0.0001199	0.7049757	61	C1QC/C1QB/CLU/C1QA/IGHV1-2/…
REACTOME_INITIAL_TRIGGERING_OF_COMPLEMENT	2.334721	0.7274181	0.0000004	0.0003283	0.6749629	51	C1QC/C1QB/C1QA/IGHV1-2/IGLV3-21/…
REACTOME_CREATION_OF_C4_AND_C2_ACTIVATORS	2.300390	0.7276567	0.0000010	0.0004966	0.6594444	47	C1QC/C1QB/C1QA/IGHV1-2/IGLV3-21/…
REACTOME_INTERFERON_SIGNALING	1.912553	0.5074714	0.0000025	0.0009754	0.6272567	158	FCGR1A/IFI27/IFITM2/HERC5/MT2A/…
REACTOME_INTERFERON_ALPHA_BETA_SIGNALING	2.138732	0.6781072	0.0000342	0.0089123	0.5573322	46	IFITM2/IFITM3/IFITM1/RSAD2/IFI6/…
REACTOME_IMMUNOREGULATORY_INTERACTIONS_BETWEEN_A_LYMPHOID_AND_A_NON_LYMPHOID_CELL	1.946263	0.5333351	0.0000416	0.0092853	0.5573322	122	FCGR1A/IFITM1/IGHV1-2/IGLV3-21/IGLV3-1/…
REACTOME_BINDING_AND_UPTAKE_OF_LIGANDS_BY_SCAVENGER_RECEPTORS	1.933008	0.5894729	0.0001498	0.0208972	0.5188481	59	MARCO/IGHV1-2/IGLV3-21/IGLV3-1/HP/…
REACTOME_FCGR_ACTIVATION	1.975826	0.6105527	0.0002215	0.0266277	0.5188481	52	FCGR1A/IGHV1-2/IGLV3-21/IGLV3-1/IGLV3-19/…
REACTOME_FORMATION_OF_FIBRIN_CLOT_CLOTTING_CASCADE	1.853646	0.8910449	0.0004117	0.0459580	0.4984931	8	SERPING1/PF4/F5/F13A1
REACTOME_ANTI_INFLAMMATORY_RESPONSE_FAVOURING_LEISHMANIA_PARASITE_INFECTION	1.714911	0.4837702	0.0005785	0.0602809	0.4772708	104	FCGR1A/ADM/IGHV1-2/IGLV3-21/IGLV3-1/…
REACTOME_M_PHASE	-1.438456	-0.3445277	0.0008100	0.0744759	0.4772708	238	AAAS/PPP2R2D/SDCCAG8/AHCTF1/HAUS1/…
REACTOME_GLOBAL_GENOME_NUCLEOTIDE_EXCISION_REPAIR_GG_NER	-1.817091	-0.5111542	0.0001604	0.0208972	0.5188481	74	POLD2/XPC/PCNA/POLD3/UBE2N/…
REACTOME_RHOBTB_GTPASE_CYCLE	-2.002547	-0.6679343	0.0000925	0.0160571	0.5384341	30	CCT2/VIM/HNRNPC/STK38/COPS2/…
REACTOME_RHOBTB1_GTPASE_CYCLE	-2.056884	-0.7621998	0.0000516	0.0100903	0.5573322	19	CCT2/VIM/HNRNPC/STK38/COPS2/…

11.2.3.5.3.5 cluster_5

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_INTERFERON_SIGNALING	2.208966	0.5825714	0.0e+00	0.0000496	0.7195128	142	IFI27/IFIT1/IFITM1/IFI6/ISG15/…
REACTOME_INTERFERON_ALPHA_BETA_SIGNALING	2.520403	0.7853826	1.7e-06	0.0013139	0.6435518	42	IFIT1/IFITM1/IFI6/ISG15/MX1/…

11.2.3.5.3.6 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_INTERFERON_SIGNALING	2.321979	0.6088425	0.0000000	0.0000172	0.7477397	138	IFI27/IFI6/IFITM1/IFITM3/XAF1/…
REACTOME_INTERFERON_ALPHA_BETA_SIGNALING	2.543723	0.7886879	0.0000001	0.0000974	0.6901325	43	IFI6/IFITM1/IFITM3/XAF1/MX1/…
REACTOME_INITIAL_TRIGGERING_OF_COMPLEMENT	2.207567	0.8119057	0.0000623	0.0316444	0.5384341	19	IGLV1-40/C1QB/IGLV3-21/IGKV3-20/FCN1/…
REACTOME_COMPLEMENT_CASCADE	2.266258	0.7768909	0.0001013	0.0385509	0.5384341	24	IGLV1-40/C1QB/IGLV3-21/IGKV3-20/FCN1/…
REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM	1.593366	0.3619088	0.0001716	0.0522715	0.5188481	386	IFI27/IFI6/IFITM1/IFITM3/XAF1/…

11.2.3.6 CP_KEGG

11.2.3.6.1 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.6.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.6.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Mild and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Mild relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.2.3.6.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_GRAFT_VERSUS_HOST_DISEASE	2.014249	0.6957889	0.0003162	0.0292455	0.4984931	32	FASLG/KIR3DL1/HLA-DRB5/HLA-DOB/HLA-DRB1/…
KEGG_RIBOSOME	-2.052827	-0.5831366	0.0000021	0.0003909	0.6272567	84	RPL13/RPL37A/RPS27/RPL10A/RPL4/…

11.2.3.6.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_ALZHEIMERS_DISEASE	-1.559612	-0.4242751	0.0031687	0.0982283	0.4317077	124	COX6C/COX7B/NDUFA4/TNF/UQCR11/…
KEGG_OXIDATIVE_PHOSPHORYLATION	-1.603407	-0.4425057	0.0023696	0.0982283	0.4317077	112	COX6C/COX7B/NDUFA4/UQCR11/COX7C/…
KEGG_ASTHMA	-1.722090	-0.6940613	0.0027546	0.0982283	0.4317077	16	TNF/HLA-DPB1/HLA-DRA
KEGG_PARKINSONS_DISEASE	-1.735147	-0.4814700	0.0002654	0.0164542	0.4984931	106	COX6C/COX7B/NDUFA4/UQCR11/COX7C/…
KEGG_CARDIAC_MUSCLE_CONTRACTION	-1.992528	-0.7030220	0.0000481	0.0044722	0.5573322	30	COX6C/COX7B/UQCR11/COX7C/ATP1B3/…
KEGG_RIBOSOME	-2.254719	-0.6477164	0.0000000	0.0000007	0.7614608	84	RPL8/RPL7A/RPS25/RPL35A/RPS17/…

11.2.3.6.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS	1.894826	0.534839	0.0003954	0.0731415	0.4984931	71	H3C2/H3C10/H3C7/H3C3/HLA-DRB5/…

11.2.3.6.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS	2.272672	0.6778046	3.00e-07	0.0000508	0.6749629	65	C1QC/C1QB/FCGR1A/C1QA/CD40/…
KEGG_COMPLEMENT_AND_COAGULATION_CASCADES	2.074830	0.8554510	6.37e-05	0.0059279	0.5384341	15	C1QC/C1QA/SERPING1/CR1/F5/…
KEGG_PRION_DISEASES	2.036097	0.7755857	1.67e-04	0.0103536	0.5188481	21	C1QC/C1QB/C1QA/IL1B/CCL5/…

11.2.3.6.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_HUNTINGTONS_DISEASE	-1.683514	-0.4568229	0.0005711	0.0303615	0.4772708	125	SDHB/NDUFS4/APAF1/NDUFB9/NDUFS7/…
KEGG_OXIDATIVE_PHOSPHORYLATION	-1.691128	-0.4645116	0.0006600	0.0303615	0.4772708	106	SDHB/NDUFS4/NDUFB9/NDUFS7/NDUFB7/…
KEGG_PARKINSONS_DISEASE	-1.725441	-0.4776757	0.0005921	0.0303615	0.4772708	102	SDHB/NDUFS4/APAF1/UBE2J2/NDUFB9/…
KEGG_LYSOSOME	-1.859359	-0.5351416	0.0000448	0.0082472	0.5573322	83	LAPTM4A/LAPTM5/AP3B1/CD63/GNS/…

11.2.3.7 TFT_GTRD

11.2.3.7.1 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.7.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.7.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Mild and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Mild relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.2.3.7.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
SMCHD1_TARGET_GENES	1.922524	0.6866805	0.0008621	0.0296569	0.4772708	25	NBPF1/TRGV5P/LERFS/TRAV10/MT-ND6/…
HOXC11_TARGET_GENES	2.152064	0.7293295	0.0010486	0.0338166	0.4550599	32	S100B/DUSP6
ZNF101_TARGET_GENES	-1.436313	-0.3600726	0.0062072	0.0889699	0.4070179	190	NEMF/CNEP1R1/FBXO8/SNRPD1/ENTPD1-AS1/…
PCGF1_TARGET_GENES	-1.380740	-0.3270025	0.0045587	0.0823864	0.4070179	337	RCOR3/ZNF280D/DCTN1/PRKAG2/GK5/…
ZBED4_TARGET_GENES	-1.486954	-0.3917365	0.0033962	0.0700976	0.4317077	139	MFSD11/SEC61G/VPS26C/FAM53C/ETS2/…
FOXE1_TARGET_GENES	-1.386887	-0.3170301	0.0005339	0.0229570	0.4772708	508	MFSD11/RPL13/BABAM1/ATF5/IER2/…
DLX6_TARGET_GENES	-1.482054	-0.3440281	0.0001914	0.0123457	0.5188481	429	GMPR2/NEMF/CSNK2B/COX7A2L/VASP/…
PHF2_TARGET_GENES	-1.370358	-0.2998527	0.0000684	0.0058824	0.5384341	1016	RPL13/RCOR3/POP7/GALNS/CSNK2B/…
RFX7_TARGET_GENES	-1.554477	-0.3653217	0.0000481	0.0049651	0.5573322	379	GMPR2/POP7/C12orf4/ATF5/NEMF/…
NKX2_5_TARGET_GENES	-1.425889	-0.3148294	0.0000334	0.0043071	0.5573322	830	MFSD11/METTL3/COX7A2L/PALLD/FBXO8/…
ELF2_TARGET_GENES	-1.381361	-0.2999203	0.0000063	0.0016220	0.6105269	1206	BABAM1/C12orf4/IER2/FBXO8/CHFR/…
NFE2L1_TARGET_GENES	-1.428544	-0.3084065	0.0000002	0.0000780	0.6901325	1398	GMPR2/MFSD11/RMC1/BABAM1/BTG2/…

11.2.3.7.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
ZNF236_TARGET_GENES	-1.479432	-0.3940172	0.0044454	0.0997317	0.4070179	156	MEF2A/COX6C/SLX4IP/TSPAN31/MALAT1/…
MCM3_TARGET_GENES	-1.546546	-0.4270079	0.0032270	0.0756886	0.4317077	115	FTSJ3/SLX4IP/ZNF432/MRPL52/DEDD/…
ZNF197_TARGET_GENES	-1.395317	-0.3383996	0.0030195	0.0741930	0.4317077	376	BZW2/DHX40/SLX4IP/NDUFA4/SIPA1L3/…
TERF2_TARGET_GENES	-1.589418	-0.4595987	0.0024678	0.0636705	0.4317077	87	TMBIM1/ATP5MJ/C1orf74/NF1/TBL1XR1/…
CIITA_TARGET_GENES	-1.348953	-0.3065996	0.0003165	0.0183378	0.4984931	970	N4BP1/FTSJ3/SLX4IP/ZNF432/BTN2A2/…
FXR1_TARGET_GENES	-1.375623	-0.3175854	0.0003198	0.0183378	0.4984931	716	COX16/CNOT8/FTSJ3/NDUFA4/SIPA1L3/…
BANP_TARGET_GENES	-1.432976	-0.3341123	0.0002902	0.0183378	0.4984931	573	COX7B/PPM1G/SLX4IP/SPTY2D1/OFD1/…
RUVBL1_TARGET_GENES	-1.730227	-0.4706364	0.0000899	0.0115936	0.5384341	132	RPS14/FTSJ3/SLX4IP/CCDC47/FMC1/…
NFRKB_TARGET_GENES	-1.362722	-0.3038247	0.0000094	0.0024168	0.5933255	1489	MEF2A/COX16/FTSJ3/COX7B/PRMT1/…
DLX4_TARGET_GENES	-1.523407	-0.3536348	0.0000055	0.0024168	0.6105269	648	COX16/FTSJ3/PAIP2B/NDUFA4/CSPP1/…

11.2.3.7.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
PSMB5_TARGET_GENES	1.448105	0.3486876	0.0042410	0.0989136	0.4070179	214	H1-4/H1-2/H3C10/PMAIP1/DDIT4/…
ZNF213_TARGET_GENES	-1.388793	-0.3375391	0.0041518	0.0989136	0.4070179	384	DNAJB12/KXD1/GLYR1/GLOD4/RFX1/…
IRF5_TARGET_GENES	-1.368287	-0.3250624	0.0037916	0.0978228	0.4317077	538	RAB4B/COMMD1/RABL2B/CREBZF/RFC3/…
NPM1_TARGET_GENES	-1.412006	-0.3529889	0.0036984	0.0978228	0.4317077	275	SEPTIN2/CSPP1/IREB2/DUSP12/COA1/…
PITX1_TARGET_GENES	-1.459145	-0.3664280	0.0026417	0.0801821	0.4317077	258	RAB4B/GUSB/DNAJB12/COMMD1/ATG9A/…
GLI3_TARGET_GENES	-1.375675	-0.3260530	0.0021700	0.0699816	0.4317077	546	RAB4B/DLG1/EDC3/MUTYH/SEPTIN2/…
NFE2L3_TARGET_GENES	-1.720815	-0.9286220	0.0010710	0.0425119	0.4550599	5	TEP1
ARID5B_TARGET_GENES	-1.380678	-0.3215926	0.0007853	0.0377204	0.4772708	690	GUSB/CRBN/XRCC5/ZNF37A/PLD3/…
GUCY1B1_TARGET_GENES	-1.422982	-0.3383012	0.0006488	0.0371979	0.4772708	509	GUSB/PLD3/DRAM2/WASHC5/ADCK1/…
ZNF2_TARGET_GENES	-1.403596	-0.3256464	0.0002489	0.0256816	0.4984931	757	DNAJB12/CRBN/NCBP2/TBL3/CCT2/…
TAF9B_TARGET_GENES	-1.529550	-0.3690157	0.0000568	0.0146545	0.5573322	425	MRPL35/COMMD1/SEPTIN2/IMMP1L/WDR74/…

11.2.3.7.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
PSMB5_TARGET_GENES	1.655767	0.4197568	0.0003547	0.0382911	0.4984931	219	ID1/ID2/SNHG5/ADM/HSPA1B/…
ZNF592_TARGET_GENES	-1.215365	-0.2552286	0.0014816	0.0707716	0.4550599	1253	AAAS/PPP2R2D/PDIA5/CALCOCO1/GOSR2/…
BARX2_TARGET_GENES	-1.232782	-0.2586457	0.0009855	0.0707716	0.4550599	1292	TRIM26/WBP11/CCT2/RAB11B-AS1/RAD51B/…
SUPT20H_TARGET_GENES	-1.254619	-0.2653314	0.0008977	0.0707716	0.4772708	1025	ILF3-DT/TMEM106B/AAAS/WBP11/RAB11B-AS1/…
E2F5_TARGET_GENES	-1.261834	-0.2719733	0.0016490	0.0707716	0.4550599	782	C19orf53/PTP4A3/STAT5B/C11orf21/RAD51B/…
INSM2_TARGET_GENES	-1.348438	-0.2996003	0.0013726	0.0707716	0.4550599	500	ILF3-DT/CCT2/RAB11B-AS1/WARS1/ARPP19/…
NPM1_TARGET_GENES	-1.408016	-0.3307216	0.0016186	0.0707716	0.4550599	281	ABCD4/COPA/YBX1/THOC1/NFATC3/…
E2F2_TARGET_GENES	-1.267328	-0.2679354	0.0003718	0.0382911	0.4984931	1040	C19orf53/ILF3-DT/FRAT1/AIFM1/ITSN1/…
ZNF830_TARGET_GENES	-1.641045	-0.4030440	0.0001248	0.0214185	0.5188481	196	ABCD4/EIF6/THOC1/NDUFB7/ZSCAN30/…
ZNF407_TARGET_GENES	-1.279812	-0.2668954	0.0000466	0.0163953	0.5573322	1396	ILF3-DT/TMEM106B/AAAS/SYVN1/ITSN1/…
ELF2_TARGET_GENES	-1.305142	-0.2747181	0.0000637	0.0163953	0.5384341	1167	ZUP1/RAB11B-AS1/MFN1/DPH3/GOSR2/…

11.2.3.7.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
NFE2L1_TARGET_GENES	-1.415033	-0.3228922	8.2e-06	0.0041929	0.5933255	1023	KIDINS220/CHCHD3/CAP1/TRMT112/TNPO3/…

11.2.3.8 TFT_TFT_Legacy

11.2.3.8.1 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.8.2 Summaryplot: Healthy

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Healthy. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.2.3.8.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Mild and Healthy for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Mild relative to Healthy, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.2.3.8.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
SCGGAAGY_ELK1_02	-1.332276	-0.2913055	0.0001058	0.0645449	0.5384341	1074	GMPR2/BABAM1/CSNK2B/YTHDF2/CCP110/…

11.2.3.8.3.2 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KRCTCNNNNMANAGC_UNKNOWN	2.460644	0.7779709	0.00e+00	0.0000280	0.7195128	37	H1-4/H1-2/H3C2/H3C10/H3C7/…
TTTNNANAGCYR_UNKNOWN	2.032007	0.5781782	3.45e-05	0.0105254	0.5573322	65	H1-4/H1-2/H3C2/H3C10/H3C7/…
SCGGAAGY_ELK1_02	-1.332961	-0.3048625	3.16e-04	0.0642453	0.4984931	1042	RAB4B/PTPN2/KXD1/PPP1R35/UFD1/…

11.2.3.8.3.3 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
FREAC4_01	1.856993	0.5632482	0.0007946	0.0973142	0.4772708	62	ID2/CDKN1A/CAMK1D/CITED2/CTCF/…
GATA_Q6	1.854005	0.5680466	0.0005867	0.0973142	0.4772708	60	CLU/ADM/LCAT/CACNA2D3/STON2/…
E12_Q6	1.794529	0.4997787	0.0004455	0.0973142	0.4984931	105	C1QC/CAMK1D/FAM53C/MIR22HG/ACAP1/…
E47_01	1.710654	0.4642730	0.0007977	0.0973142	0.4772708	123	C1QC/CAMK1D/CCNYL1/EPHB2/PPTC7/…
MYOD_01	1.677834	0.4629032	0.0009672	0.0983319	0.4772708	114	C1QC/FAM53C/MIR22HG/TICAM1/ACAP1/…
CCCNNNNNNAAGWT_UNKNOWN	-1.754053	-0.5005989	0.0002481	0.0973142	0.4984931	71	IRF9/NCDN/CPEB4/DHRS9/MRPL49/…

11.3 Covid_Critical_vs_Covid_Mild

11.3.1 Number of significant DEGs across conditions in each cluster

Here we check number of differentially expressed genes (DEGs) after applying some statistical thresholds:

Adjusted P value < 0.1
Log Fold Change > +/- 0
Proportion of cells expressing gene in condition A > 0.1 if LFC is positive
Proportion of cells expressing gene in condition B > 0.1 if LFC is negative

Differential expression compares genes in condition A (left) versus condition B (right). The left column indicates the number of genes upregulated in the left condition, while the right column indicates genes upregulated in the right condition.

Finally, the thresholds above do not affect downstream results from pathway analysis, they are just meant to count the number of DEGs. Downstream usage of DEGs can use these thresholds, or you can choose other appropriate cutoffs.

Cluster	Covid_Critical_high	Covid_Mild_high
cluster_1	193	66
cluster_2	4	1
cluster_3	9	5
cluster_4	60	51
cluster_5	0	0
cluster_6	6	5
cluster_7	29	11

11.3.2 Per-cluster heatmaps of all DEGs

To ensure the differential expression results are robust, it is helpful to inspect all DEGs by visualizing them in a heatmap. For each cluster, we compare the DEGs at both the single cell level, and either the pseudobulk level (if we used pseudobulk_edgeR) or the average RISC value level (if using wilcox).

11.3.2.1 Cluster_1

11.3.2.1.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.3.2.1.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.3.2.2 Cluster_2

11.3.2.2.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.3.2.2.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.3.2.3 Cluster_3

11.3.2.3.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.3.2.3.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.3.2.4 Cluster_4

11.3.2.4.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.3.2.4.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.3.2.5 Cluster_6

11.3.2.5.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.3.2.5.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.3.2.6 Cluster_7

11.3.2.6.1 Differentially Expressed Gene Heatmap for all cells in this cluster

Here, we plot all significant DEGs for all cells in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.3.2.6.2 Differentially Expressed Gene Heatmap plotted at pseudobulk level for replicates for this cluster

Here, we plot all significant DEGs for pseudobulked replicates in this cluster from condition A vs condition B. Condition A is denoted with positive fold change.

11.3.3 Gene set enrichment analysis across conditions

Pathway analysis for the cross-condition analysis is performed for overexpressed and underexpressed genes for each cluster. This is done via Gene Set Enrichment Analysis (GSEA) (Subramanian et al 2005).

GSEA is preferred over other pathway analysis such as fisher tests or chi-square tests because it does not require making arbitrary cutoffs to the number of DEGs and takes into account how strongly differentially expressed each gene may be. For the latter, data-driven gene-specific weight is applied. We use a standard weighting method of -log10(P-value) * sign of Log Fold Change.

The pathways we choose in pathway analysis are derived from the Molecular Signatures Database (MSIGDB) where they are sorted by categories, such as Gene Ontology (GO) Biological Process, GO Molecular Function, KEGG, Reactome, etc. These are databases that annotate genes by function or molecular pathway.

11.3.3.1 HALLMARK

11.3.3.1.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.1.2 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.1.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Covid_Mild for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Covid_Mild, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.3.3.1.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_E2F_TARGETS	2.737946	0.7895349	0.0000000	0.0000000	1.4885397	192	CENPM/MCM4/TK1/RRM2/MKI67/…
HALLMARK_G2M_CHECKPOINT	2.544265	0.7445422	0.0000000	0.0000000	1.2210538	172	GINS2/MKI67/MCM2/STMN1/CKS2/…
HALLMARK_MYC_TARGETS_V1	2.075492	0.5995023	0.0000000	0.0000000	0.7749390	190	TYMS/MCM4/MCM2/MCM7/DUT/…
HALLMARK_MTORC1_SIGNALING	1.817999	0.5273447	0.0000042	0.0000530	0.6105269	180	MCM4/RRM2/MCM2/DHFR/SHMT2/…
HALLMARK_MITOTIC_SPINDLE	1.705572	0.5017886	0.0001141	0.0011410	0.5384341	164	LMNB1/BIRC5/TOP2A/SMC4/YWHAE/…
HALLMARK_APOPTOSIS	1.747477	0.5305002	0.0001396	0.0011632	0.5188481	126	HMGB2/TOP2A/BAX/GSTM1/CD38/…
HALLMARK_ESTROGEN_RESPONSE_LATE	1.810981	0.5915113	0.0004493	0.0032092	0.4984931	75	PRSS23/FABP5/RBBP8/FOS/HPRT1/…
HALLMARK_SPERMATOGENESIS	1.786145	0.6065984	0.0010041	0.0062753	0.4550599	55	CDKN3/NCAPH/TKTL1/KIF2C/CCNB2/…
HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION	1.689822	0.5688202	0.0018845	0.0104697	0.4550599	60	LGALS1/JUN/MAGEE1/GLIPR1/AREG/…
HALLMARK_MYC_TARGETS_V2	1.724708	0.5889784	0.0045457	0.0227287	0.4070179	52	MCM4/PLK1/MCM5/PLK4/UNG/…

11.3.3.1.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_TNFA_SIGNALING_VIA_NFKB	1.730964	0.4704992	0.0001438	0.0071898	0.5188481	130	RHOB/JUN/FOS/TNIP2/IRF1/…
HALLMARK_INTERFERON_ALPHA_RESPONSE	1.579154	0.4599315	0.0033237	0.0830925	0.4317077	85	IFITM3/IRF1/WARS1/IFI30/IFIT3/…
HALLMARK_CHOLESTEROL_HOMEOSTASIS	1.597827	0.4965563	0.0083353	0.0912447	0.3807304	57	FABP5/ATF3/PMVK/FDPS/SCD/…
HALLMARK_MYOGENESIS	1.572948	0.4668065	0.0070259	0.0912447	0.4070179	74	CFD/PTP4A3/PLXNB2/FXYD1/ACSL1/…
HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION	1.547222	0.4865862	0.0122976	0.0912447	0.3807304	51	RHOB/EMP3/JUN/LGALS1/P3H1/…
HALLMARK_IL2_STAT5_SIGNALING	1.479236	0.4056574	0.0111477	0.0912447	0.3807304	118	IFITM3/RHOB/COCH/SOCS1/KLF6/…
HALLMARK_INTERFERON_GAMMA_RESPONSE	1.391599	0.3679859	0.0127743	0.0912447	0.3807304	164	IFITM3/IRF1/MTHFD2/WARS1/IFI30/…

11.3.3.1.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_TNFA_SIGNALING_VIA_NFKB	1.908383	0.5229669	0.0000018	0.0000917	0.6435518	126	IFNGR2/FOS/SGK1/JUN/SOCS3/…
HALLMARK_IL2_STAT5_SIGNALING	1.764689	0.4796179	0.0000243	0.0006084	0.5756103	134	IFITM3/SOCS2/PIM1/CISH/PTGER2/…
HALLMARK_INTERFERON_GAMMA_RESPONSE	1.687963	0.4452124	0.0001215	0.0020244	0.5384341	157	IFITM3/PIM1/IFIT1/SOCS3/TNFAIP3/…
HALLMARK_HYPOXIA	1.788345	0.5047328	0.0002017	0.0020807	0.5188481	97	PIM1/FOS/JUN/TNFAIP3/HK2/…
HALLMARK_ALLOGRAFT_REJECTION	1.659341	0.4509858	0.0002081	0.0020807	0.5188481	134	IFNGR2/EIF5A/CD40LG/GZMA/SOCS1/…
HALLMARK_INFLAMMATORY_RESPONSE	1.685743	0.4751696	0.0007592	0.0063266	0.4772708	101	IFNGR2/PTGER2/CCL5/SLC31A2/TNFRSF1B/…
HALLMARK_IL6_JAK_STAT3_SIGNALING	1.754310	0.5574797	0.0026213	0.0187235	0.4317077	49	PIM1/IFNGR2/JUN/SOCS3/SOCS1/…
HALLMARK_ESTROGEN_RESPONSE_LATE	1.656993	0.4950897	0.0033919	0.0188437	0.4317077	68	FOS/CISH/SGK1/AREG/SIAH2/…
HALLMARK_ESTROGEN_RESPONSE_EARLY	1.547493	0.4580171	0.0071503	0.0325014	0.4070179	71	FOS/UGCG/AREG/SIAH2/PPIF/…
HALLMARK_SPERMATOGENESIS	1.568080	0.5051500	0.0133072	0.0554466	0.3807304	47	PHF7/CCNB2/SPATA6/PCSK1N/GSTM3/…
HALLMARK_DNA_REPAIR	-1.477229	-0.3700879	0.0057255	0.0286277	0.4070179	133	TSG101/GTF2A2/POLR2G/POLR3C/NFX1/…
HALLMARK_PROTEIN_SECRETION	-1.608506	-0.4373577	0.0032887	0.0188437	0.4317077	86	TSG101/VAMP3/M6PR/ATP7A/GNAS/…

11.3.3.1.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HALLMARK_INTERFERON_GAMMA_RESPONSE	1.918113	0.4947319	0.0000008	0.0000398	0.6594444	171	CXCL10/CD86/PIM1/CXCL9/HLA-DRB1/…
HALLMARK_IL2_STAT5_SIGNALING	1.989652	0.5280380	0.0000020	0.0000494	0.6272567	131	CXCL10/SLC39A8/CD86/PIM1/CCND3/…
HALLMARK_ALLOGRAFT_REJECTION	1.785129	0.4730931	0.0001526	0.0025437	0.5188481	134	FCGR2B/CD86/CCND3/CXCL9/HLA-DMB/…
HALLMARK_G2M_CHECKPOINT	1.693166	0.4408433	0.0002619	0.0032734	0.4984931	156	BIRC5/MKI67/PTTG1/TROAP/NOTCH2/…
HALLMARK_INFLAMMATORY_RESPONSE	1.641244	0.4393283	0.0011885	0.0084889	0.4550599	124	CXCL10/CD82/CXCL9/GNA15/PTGER2/…
HALLMARK_TNFA_SIGNALING_VIA_NFKB	1.639965	0.4295291	0.0009050	0.0084889	0.4772708	149	CXCL10/TUBB2A/KLF10/DUSP2/ATP2B1/…
HALLMARK_E2F_TARGETS	1.593703	0.4101575	0.0010971	0.0084889	0.4550599	174	BIRC5/MKI67/PTTG1/CDKN3/HMGB3/…
HALLMARK_KRAS_SIGNALING_UP	1.671064	0.4623526	0.0026793	0.0167456	0.4317077	101	RETN/CXCL10/CD37/MMD/GYPC/…
HALLMARK_IL6_JAK_STAT3_SIGNALING	1.589935	0.4842787	0.0135668	0.0753711	0.3807304	61	CXCL10/ITGA4/PIM1/CXCL9/CD9/…

11.3.3.2 GO_BP

11.3.3.2.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.2.2 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.2.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Covid_Mild for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Covid_Mild, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.3.3.2.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_PROTEIN_DNA_COMPLEX_ASSEMBLY	2.277259	0.6811043	0.0000000	0.0000000	0.9214260	148	H3C2/CENPN/MCM2/ASF1B/CDC45/…
GOBP_DNA_CONFORMATION_CHANGE	2.166534	0.6164765	0.0000000	0.0000000	0.9214260	237	H3C2/GINS2/MCM4/MCM2/ASF1B/…
GOBP_NUCLEOSOME_ASSEMBLY	2.314559	0.7487408	0.0000000	0.0000000	0.8634154	83	H3C2/MCM2/ASF1B/HMGB2/CENPW/…
GOBP_MEIOTIC_CELL_CYCLE	2.218469	0.6631053	0.0000000	0.0000000	0.8390889	142	CKS2/PKMYT1/CDC20/RAD51/NCAPH/…
GOBP_PROTEIN_DNA_COMPLEX_SUBUNIT_ORGANIZATION	2.132524	0.6188807	0.0000000	0.0000001	0.8140358	186	H3C2/CENPN/MCM2/ASF1B/CDC45/…
GOBP_DNA_PACKAGING	2.140101	0.6272248	0.0000000	0.0000002	0.8140358	173	H3C2/MCM2/ASF1B/HMGB2/NCAPH/…
GOBP_NUCLEOSOME_ORGANIZATION	2.184606	0.6615645	0.0000000	0.0000003	0.8012156	125	H3C2/MCM2/ASF1B/HMGB2/CENPW/…
GOBP_DNA_REPLICATION_INITIATION	2.275093	0.8575246	0.0000000	0.0000008	0.7749390	31	MCM4/MCM2/MCM7/CDC45/MCM10/…
GOBP_CELL_CYCLE_DNA_REPLICATION	2.221539	0.8091916	0.0000000	0.0000092	0.7195128	39	MCM4/MCM2/PCNA/CDC45/RAD51/…
GOBP_DNA_UNWINDING_INVOLVED_IN_DNA_REPLICATION	2.177817	0.8761540	0.0000002	0.0000477	0.6901325	22	GINS2/MCM4/MCM2/MCM7/CDC45/…
GOBP_REGULATION_OF_ACTIN_NUCLEATION	-1.865035	-0.7056101	0.0009177	0.0549241	0.4772708	20	WAS/WASF2/AP1AR/ARFIP1/CYFIP1/…
GOBP_URATE_METABOLIC_PROCESS	-1.584348	-0.9831093	0.0003745	0.0271984	0.4984931	3	PRPS1/ABCG2
GOBP_REGULATION_OF_ARP2_3_COMPLEX_MEDIATED_ACTIN_NUCLEATION	-1.963158	-0.7789338	0.0003658	0.0268287	0.4984931	16	WAS/WASF2/AP1AR/ARFIP1/CYFIP1/…

11.3.3.2.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_ADAPTIVE_IMMUNE_RESPONSE	2.133270	0.5206155	0.0000000	0.0000000	1.0476265	358	IGHV5-10-1/IGHV3-49/IGKV6-21/IGLV4-3/IGLV5-37/…
GOBP_REGULATION_OF_B_CELL_ACTIVATION	2.344807	0.6307548	0.0000000	0.0000000	0.9865463	150	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOBP_COMPLEMENT_ACTIVATION	2.519309	0.7459768	0.0000000	0.0000000	0.9436322	70	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOBP_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN	2.487493	0.7325738	0.0000000	0.0000000	0.9436322	74	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOBP_POSITIVE_REGULATION_OF_B_CELL_ACTIVATION	2.354249	0.6602114	0.0000000	0.0000000	0.9101197	106	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOBP_B_CELL_RECEPTOR_SIGNALING_PATHWAY	2.330385	0.6572601	0.0000000	0.0000000	0.8870750	103	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOBP_MEMBRANE_INVAGINATION	2.333606	0.6574626	0.0000000	0.0000000	0.8753251	100	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOBP_B_CELL_MEDIATED_IMMUNITY	2.200900	0.5948299	0.0000000	0.0000000	0.8753251	146	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOBP_PHAGOCYTOSIS_RECOGNITION	2.397788	0.7209858	0.0000000	0.0000001	0.8390889	64	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOBP_CELL_RECOGNITION	2.128607	0.5962921	0.0000000	0.0000022	0.7614608	108	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOBP_RIBONUCLEOPROTEIN_COMPLEX_BIOGENESIS	-1.470557	-0.3363683	0.0006050	0.0916945	0.4772708	361	MRPL1/EBNA1BP2/SRPK2/USP39/METTL18/…
GOBP_POSITIVE_REGULATION_OF_ORGANELLE_ORGANIZATION	-1.474498	-0.3452834	0.0005451	0.0843482	0.4772708	311	TNFSF10/CALCOCO2/WRAP73/SLC25A4/ANXA1/…
GOBP_ORGANONITROGEN_COMPOUND_BIOSYNTHETIC_PROCESS	-1.282478	-0.2719349	0.0004705	0.0760287	0.4984931	1090	RPL30/SVIP/METTL18/PIGB/RB1CC1/…
GOBP_CYTOPLASMIC_TRANSLATION	-1.859527	-0.4822311	0.0000284	0.0055584	0.5756103	136	RPL30/FMR1/RPS5/DHX36/RPL22/…
GOBP_CELLULAR_MACROMOLECULE_BIOSYNTHETIC_PROCESS	-1.369944	-0.2937096	0.0000220	0.0045352	0.5756103	997	RPL30/NASP/SVIP/METTL18/PIGB/…

11.3.3.2.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_ADAPTIVE_IMMUNE_RESPONSE	1.875645	0.4546636	0.0000000	0.0000006	0.8266573	403	TRBV18/TRAV27/HLA-DQB1/TRBV10-1/IGKV2-30/…
GOBP_RESPONSE_TO_BACTERIUM	1.763266	0.4334760	0.0000002	0.0003109	0.6901325	327	TRAV27/FOS/GNLY/TRAV25/IGHV3-49/…
GOBP_NEGATIVE_REGULATION_OF_VIRAL_PROCESS	1.929180	0.5758266	0.0000281	0.0161169	0.5756103	69	IFITM3/IFIT1/OAS2/TRIM11/CCL5/…
GOBP_RECEPTOR_SIGNALING_PATHWAY_VIA_STAT	1.875341	0.5427315	0.0000541	0.0268474	0.5573322	82	SOCS2/SOCS3/SOCS1/TNFRSF18/CCL5/…
GOBP_RESPONSE_TO_MOLECULE_OF_BACTERIAL_ORIGIN	1.692554	0.4483018	0.0000993	0.0389238	0.5384341	164	FOS/PTGER2/TNFAIP3/ARID5A/CCL5/…
GOBP_PLASMA_CELL_DIFFERENTIATION	1.733595	0.9494068	0.0002125	0.0626707	0.5188481	5	LGALS1/XBP1/IL10
GOBP_NEGATIVE_REGULATION_OF_VIRAL_GENOME_REPLICATION	1.931641	0.6115060	0.0002622	0.0650605	0.4984931	45	IFITM3/IFIT1/OAS2/CCL5/MX1/…
GOBP_RESPONSE_TO_ALCOHOL	1.753322	0.4782822	0.0002908	0.0658004	0.4984931	123	CLDN5/FOS/SGK1/CDO1/PTGER2/…
GOBP_TYROSINE_PHOSPHORYLATION_OF_STAT_PROTEIN	1.866332	0.6225493	0.0004476	0.0841860	0.4984931	36	SOCS3/SOCS1/TNFRSF18/CCL5/IL6ST/…
GOBP_RESPONSE_TO_MINERALOCORTICOID	1.884710	0.7786223	0.0006691	0.0976717	0.4772708	14	FOS/SGK1/CYBA
GOBP_TRIPEPTIDE_TRANSMEMBRANE_TRANSPORT	-1.639683	-0.9829291	0.0005507	0.0911007	0.4772708	3	ABCC1
GOBP_TRIPEPTIDE_TRANSPORT	-1.639683	-0.9829291	0.0005507	0.0911007	0.4772708	3	ABCC1
GOBP_NCRNA_3_END_PROCESSING	-1.900619	-0.6150942	0.0004283	0.0839063	0.4984931	36	DKC1/INTS2/TRMT10C/EXOSC10/INTS14/…
GOBP_LYSOSOMAL_TRANSPORT	-1.813481	-0.4889308	0.0002202	0.0626707	0.5188481	93	TSG101/RAB7A/M6PR/BIN1/CLEC16A/…
GOBP_VACUOLAR_TRANSPORT	-1.767638	-0.4497428	0.0000689	0.0302966	0.5384341	124	TSG101/RAB7A/M6PR/BIN1/CLEC16A/…

11.3.3.2.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_HUMORAL_IMMUNE_RESPONSE	2.165119	0.5878952	0.0000000	0.0000137	0.7881868	127	CXCL10/FCGR2B/C1QA/C1QB/CXCL9/…
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_OR_POLYSACCHARIDE_ANTIGEN_VIA_MHC_CLASS_II	2.221142	0.7768539	0.0000006	0.0006202	0.6594444	27	HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-DQB1/CTSD/…
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_EXOGENOUS_PEPTIDE_ANTIGEN_VIA_MHC_CLASS_II	2.237628	0.8115468	0.0000007	0.0006842	0.6594444	23	FCGR2B/HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-DQB1/…
GOBP_ANTIMICROBIAL_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_ANTIMICROBIAL_PEPTIDE	2.180670	0.7663601	0.0000024	0.0011792	0.6272567	26	CXCL10/CXCL9/S100A12/RNASE3/HMGN2/…
GOBP_MICROGLIAL_CELL_ACTIVATION	2.166229	0.7468120	0.0000042	0.0018359	0.6105269	30	CX3CR1/CST7/C1QA/LRRK2/FPR2/…
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_EXOGENOUS_ANTIGEN	2.132286	0.6986919	0.0000044	0.0018359	0.6105269	39	FCGR2B/HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-DQB1/…
GOBP_ANTIMICROBIAL_HUMORAL_RESPONSE	2.073288	0.6587770	0.0000061	0.0022711	0.6105269	47	CXCL10/CXCL9/S100A9/S100A12/RNASE3/…
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN	2.058996	0.6466273	0.0000100	0.0032469	0.5933255	50	FCGR2B/HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-G/…
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_EXOGENOUS_PEPTIDE_ANTIGEN	2.129063	0.7265962	0.0000121	0.0037956	0.5933255	32	FCGR2B/HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-DQB1/…
GOBP_DEFENSE_RESPONSE_TO_FUNGUS	2.085321	0.7405266	0.0000393	0.0086822	0.5573322	25	CX3CR1/S100A8/S100A9/S100A12/USP15/…
GOBP_TELOMERE_MAINTENANCE_VIA_RECOMBINATION	-1.856567	-0.7685224	0.0013664	0.0737555	0.4550599	13	BRCA2/SMC6/SMC5/RAD50/ERCC4
GOBP_SPHINGOMYELIN_CATABOLIC_PROCESS	-1.711820	-0.9186817	0.0010558	0.0646364	0.4550599	5	PRKCD/SMPDL3A
GOBP_NEGATIVE_REGULATION_OF_PROTEIN_LOCALIZATION_TO_NUCLEUS	-1.883167	-0.7081220	0.0007839	0.0539573	0.4772708	20	LZTS2/ILRUN/NF1/FBXO4/LATS2/…
GOBP_RNA_PHOSPHODIESTER_BOND_HYDROLYSIS_ENDONUCLEOLYTIC	-1.794260	-0.5300054	0.0005956	0.0446348	0.4772708	61	POP4/DBR1/POP5/RPP30/LACTB2/…
GOBP_ORGANELLE_TRANSPORT_ALONG_MICROTUBULE	-1.908485	-0.5737035	0.0001572	0.0206867	0.5188481	54	FBXW11/BLOC1S2/KIF5B/BLOC1S1/RHOT1/…
GOBP_VESICLE_TRANSPORT_ALONG_MICROTUBULE	-2.019859	-0.6825127	0.0001058	0.0158803	0.5384341	31	FBXW11/BLOC1S2/KIF5B/BLOC1S1/BLOC1S3/…

11.3.3.2.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_GLIAL_CELL_DEVELOPMENT	2.456649	0.7731897	0.0000002	0.0013220	0.6901325	27	S100A8/S100A9/CLU/PHGDH/ILK/…
GOBP_DEFENSE_RESPONSE_TO_FUNGUS	2.395708	0.8599855	0.0000007	0.0024346	0.6594444	16	S100A8/S100A9/S100A12/SPON2
GOBP_PEPTIDE_ANTIGEN_ASSEMBLY_WITH_MHC_CLASS_II_PROTEIN_COMPLEX	2.305183	0.8578644	0.0000032	0.0075097	0.6272567	14	HLA-DQA1/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DQB1/…
GOBP_PROTEIN_NITROSYLATION	2.080690	0.9497010	0.0000061	0.0105872	0.6105269	7	S100A8/S100A9/ATP2B4
GOBP_POSITIVE_REGULATION_OF_NF_KAPPAB_TRANSCRIPTION_FACTOR_ACTIVITY	2.054708	0.5639239	0.0000115	0.0160569	0.5933255	53	S100A8/S100A9/S100A12/CLU/TRIM14
GOBP_GLIAL_CELL_DIFFERENTIATION	2.126475	0.5973731	0.0000190	0.0217074	0.5756103	48	S100A8/S100A9/CLU/PHGDH/ILK/…
GOBP_ASTROCYTE_DEVELOPMENT	2.105190	0.8857794	0.0000515	0.0359789	0.5573322	9	S100A8/S100A9/IFNGR1/GRN
GOBP_GLIOGENESIS	1.953508	0.5294465	0.0000452	0.0359789	0.5573322	58	S100A8/S100A9/CLU/PHGDH/ILK/…
GOBP_HUMORAL_IMMUNE_RESPONSE	1.934492	0.5344818	0.0001599	0.0863855	0.5188481	50	S100A9/S100A12/CLU/POU2F2/HLA-DRB1/…
GOBP_DEFENSE_RESPONSE_TO_BACTERIUM	1.853923	0.4865660	0.0001606	0.0863855	0.5188481	68	S100A8/S100A9/MPEG1/S100A12/RNASE2/…
GOBP_REGULATION_OF_NON_CANONICAL_WNT_SIGNALING_PATHWAY	-1.563181	-0.9859825	0.0000807	0.0512741	0.5384341	3	DAB2/RNF213
GOBP_PROTEIN_DENEDDYLATION	-1.800583	-0.8718514	0.0000467	0.0359789	0.5573322	9	TOR1A/COPS7A/COPS2/COPS3/COPS5

11.3.3.2.3.6 cluster_7

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOBP_ADAPTIVE_IMMUNE_RESPONSE	2.048789	0.6588388	0.0000000	0.0000000	1.1778933	285	TRBV24-1/IGKV1-9/JCHAIN/TRBV7-6/IGHA1/…
GOBP_IMMUNE_RESPONSE	1.683739	0.5271378	0.0000000	0.0000000	1.1690700	844	GNLY/TRBV24-1/IGKV1-9/GZMB/JCHAIN/…
GOBP_RESPONSE_TO_BACTERIUM	1.744615	0.5633550	0.0000000	0.0000097	0.7614608	255	GNLY/JCHAIN/IGHA1/IGKC/FOS/…
GOBP_DEFENSE_RESPONSE_TO_BACTERIUM	1.915474	0.6553466	0.0000014	0.0014097	0.6435518	97	GNLY/JCHAIN/IGHA1/IGKC/SPON2/…
GOBP_INNATE_IMMUNE_RESPONSE	1.515235	0.4824900	0.0000010	0.0014097	0.6435518	397	GZMB/JCHAIN/IGHA1/IGKC/IFITM3/…
GOBP_HUMORAL_IMMUNE_RESPONSE	1.948609	0.6758531	0.0000048	0.0038839	0.6105269	81	GNLY/JCHAIN/IGHA1/IGKC/IGLL5/…
GOBP_ANTIMICROBIAL_HUMORAL_RESPONSE	2.032802	0.8076655	0.0000449	0.0297954	0.5573322	29	GNLY/JCHAIN/IGHA1/SPON2/IGHA2/…
GOBP_CELL_KILLING	1.832861	0.6310662	0.0000511	0.0311168	0.5573322	90	GNLY/GZMB/TYROBP/ARG1/CX3CR1/…
GOBP_COMPLEMENT_ACTIVATION	1.966954	0.8112968	0.0001437	0.0807669	0.5188481	22	IGHA1/IGKC/IGLL5/IGHA2/IGHM/…
GOBP_POSITIVE_REGULATION_OF_RESPIRATORY_BURST	1.633790	0.9845274	0.0001799	0.0938862	0.5188481	4	JCHAIN/IGHA1/IGHA2

11.3.3.3 GO_MF

11.3.3.3.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.3.2 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.3.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Covid_Mild for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Covid_Mild, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.3.3.3.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_SINGLE_STRANDED_DNA_BINDING	2.116148	0.6497663	0.0000000	0.0000606	0.7195128	111	MCM4/MCM2/MCM7/CDC45/HMGB2/…
GOMF_PEPTIDE_ANTIGEN_BINDING	2.047347	0.7822064	0.0000079	0.0044104	0.5933255	31	TRBV7-9/TRBV28/TRAV8-4/TRAV29DV5/TRAV12-1/…
GOMF_MHC_PROTEIN_BINDING	2.024207	0.7513247	0.0000267	0.0082618	0.5756103	37	TRBV7-9/CD8B/TRAV8-4/TRAV29DV5/TUBB4B/…
GOMF_SINGLE_STRANDED_DNA_HELICASE_ACTIVITY	1.977464	0.8021799	0.0000445	0.0082618	0.5573322	23	MCM4/MCM2/MCM7/RAD51/MCM5/…
GOMF_ANTIGEN_BINDING	1.843091	0.5625983	0.0000295	0.0082618	0.5756103	118	TRBV7-9/TRBV28/TRAV8-4/TRAV29DV5/TRAV12-1/…
GOMF_DNA_HELICASE_ACTIVITY	1.936528	0.6414983	0.0000732	0.0110135	0.5384341	67	MCM4/MCM2/MCM7/RAD51/MCM5/…
GOMF_DNA_REPLICATION_ORIGIN_BINDING	1.974101	0.8630608	0.0001478	0.0189926	0.5188481	15	MCM2/CDC45/MCM10/MCM5/CDC6/…
GOMF_CARBOXYLIC_ACID_BINDING	1.842536	0.6207618	0.0001707	0.0203563	0.5188481	64	PTGDS/TYMS/DHFR/FABP5/SHMT2/…
GOMF_ISOPRENOID_BINDING	1.836143	0.9050190	0.0005299	0.0465767	0.4772708	9	PTGDS/FABP5
GOMF_DNA_SECONDARY_STRUCTURE_BINDING	1.824337	0.6877532	0.0011219	0.0697218	0.4550599	33	CLSPN/HMGB2/RAD51AP1/RBBP8/HMGB3/…
GOMF_RNA_POLYMERASE_II_C_TERMINAL_DOMAIN_PHOSPHOSERINE_BINDING	-1.656665	-0.9181357	0.0011874	0.0697218	0.4550599	5	PCIF1/RTF1
GOMF_RNA_POLYMERASE_II_C_TERMINAL_DOMAIN_BINDING	-1.889651	-0.9095411	0.0000791	0.0110135	0.5384341	8	PCIF1/SCAF1/RTF1

11.3.3.3.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_IMMUNOGLOBULIN_RECEPTOR_BINDING	2.515320	0.7591970	0.0000000	0.0000000	0.9101197	61	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOMF_ANTIGEN_BINDING	2.185833	0.5928714	0.0000000	0.0000006	0.8012156	125	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOMF_OXIDOREDUCTASE_ACTIVITY	1.497780	0.3594296	0.0001513	0.0628451	0.5188481	412	MT-ND2/MTHFD2/SRD5A1/CHCHD4/MT-CYB/…
GOMF_STRUCTURAL_CONSTITUENT_OF_RIBOSOME	-2.003018	-0.5045388	0.0000067	0.0037351	0.6105269	150	RPL30/MRPL1/MRPS31/MRPL46/RPS5/…

11.3.3.3.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_CYTOKINE_ACTIVITY	2.001337	0.6480582	0.0000289	0.0240666	0.5756103	43	CMTM8/CD40LG/CCL5/CXCL13/MIF/…
GOMF_ANTIGEN_BINDING	1.855698	0.5098333	0.0000216	0.0240666	0.5756103	111	HLA-DQB1/IGKV2-30/IGHV3-49/LAG3/IGLV2-23/…
GOMF_SIGNALING_RECEPTOR_REGULATOR_ACTIVITY	1.794035	0.5069550	0.0002520	0.0666386	0.4984931	95	CMTM8/CD40LG/AREG/CCL5/EDA/…
GOMF_NUCLEAR_RECEPTOR_COACTIVATOR_ACTIVITY	-1.870840	-0.5582674	0.0004124	0.0858290	0.4984931	46	BRD8/TSG101/NCOA1/ETS1/PKN1
GOMF_GLUTATHIONE_TRANSMEMBRANE_TRANSPORTER_ACTIVITY	-1.620461	-0.9829291	0.0002802	0.0666386	0.4984931	3	ABCC1
GOMF_TRIPEPTIDE_TRANSMEMBRANE_TRANSPORTER_ACTIVITY	-1.620461	-0.9829291	0.0002802	0.0666386	0.4984931	3	ABCC1
GOMF_PEPTIDE_TRANSMEMBRANE_TRANSPORTER_ACTIVITY	-1.888030	-0.9024118	0.0001746	0.0666386	0.5188481	7	ABCC1/TAP1/SLC15A4
GOMF_INTRAMOLECULAR_TRANSFERASE_ACTIVITY	-1.958486	-0.7566921	0.0002373	0.0666386	0.5188481	17	DKC1/LSS/TRUB2/TRUB1/PGM2/…

11.3.3.3.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_PEPTIDE_BINDING	1.860633	0.4803554	0.0000038	0.0032306	0.6105269	167	FCGR2B/C1QA/HLA-DRB1/HLA-DPA1/HLA-G/…
GOMF_MHC_CLASS_II_PROTEIN_COMPLEX_BINDING	2.161612	0.7860689	0.0000106	0.0044716	0.5933255	23	HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-DQB1/HLA-DQA1/…
GOMF_MHC_PROTEIN_COMPLEX_BINDING	2.094137	0.7297481	0.0000199	0.0062596	0.5756103	29	HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-DQB1/HLA-DQA1/…
GOMF_IMMUNE_RECEPTOR_ACTIVITY	1.940081	0.5743769	0.0000496	0.0107538	0.5573322	66	CX3CR1/FCGR3A/HLA-DRB1/HLA-DPA1/HLA-DQB1/…
GOMF_CHEMOKINE_RECEPTOR_BINDING	2.044113	0.7540300	0.0000934	0.0174540	0.5384341	22	CXCL10/CX3CR1/CXCL9/CCL3L3/CCL4/…
GOMF_LONG_CHAIN_FATTY_ACID_BINDING	1.893833	0.8795587	0.0001511	0.0253955	0.5188481	9	S100A8/ALOX5AP/S100A9/STX3
GOMF_MONOCARBOXYLIC_ACID_BINDING	1.958639	0.6566511	0.0002021	0.0308884	0.5188481	34	S100A8/ALOX5AP/S100A9/RBP7/GSTM2/…
GOMF_FATTY_ACID_BINDING	1.988337	0.7334556	0.0002265	0.0317318	0.5188481	22	S100A8/ALOX5AP/S100A9/RBP7/GSTM2/…
GOMF_TOLL_LIKE_RECEPTOR_BINDING	1.880313	0.7989108	0.0007662	0.0677905	0.4772708	12	S100A8/S100A9/TLR1
GOMF_CHEMOKINE_ACTIVITY	1.840349	0.7633174	0.0013191	0.0852833	0.4550599	14	CXCL10/CXCL9/CCL4/CXCL11/CXCL8/…
GOMF_MUSCLE_ALPHA_ACTININ_BINDING	-1.694952	-0.8518713	0.0018847	0.0990080	0.4550599	7	PDLIM2/PKD2L1/PDLIM5
GOMF_GTPASE_INHIBITOR_ACTIVITY	-1.739581	-0.7999095	0.0012166	0.0830559	0.4550599	10	IQGAP2/RHOH/IPO5/IQGAP1
GOMF_ENDORIBONUCLEASE_ACTIVITY	-1.745404	-0.5422953	0.0012352	0.0830559	0.4550599	50	POP4/DBR1/POP5/RPP30/LACTB2/…
GOMF_STRUCTURAL_MOLECULE_ACTIVITY_CONFERRING_ELASTICITY	-1.710554	-0.9407178	0.0004029	0.0403182	0.4984931	5	EMILIN2/LAMC1
GOMF_GAMMA_TUBULIN_BINDING	-1.916167	-0.6931893	0.0003459	0.0403182	0.4984931	24	LYN/BRCA2/BLOC1S2/WASHC1/TUBGCP6/…

11.3.3.3.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_IMMUNE_RECEPTOR_ACTIVITY	2.371653	0.6794128	0.0000004	0.0005664	0.6749629	41	HLA-DQA1/IFNGR2/FCGR1A/HLA-DRB1/HLA-DPA1/…
GOMF_MHC_CLASS_II_PROTEIN_COMPLEX_BINDING	2.283245	0.7498025	0.0000181	0.0134317	0.5756103	23	HLA-DQA1/HLA-DRB1/HLA-DPB1/HLA-DPA1/CD4/…
GOMF_MHC_CLASS_II_RECEPTOR_ACTIVITY	2.255942	0.8883127	0.0000690	0.0290381	0.5384341	9	HLA-DQA1/HLA-DRB1/HLA-DPA1/HLA-DQB1/HLA-DRA/…
GOMF_RAGE_RECEPTOR_BINDING	1.935802	0.9524268	0.0000782	0.0290381	0.5384341	5	S100A8/S100A12/HMGB2
GOMF_MHC_PROTEIN_COMPLEX_BINDING	2.180967	0.6837297	0.0002132	0.0527555	0.5188481	26	HLA-DQA1/HLA-DRB1/HLA-DPB1/HLA-DPA1/CD4/…
GOMF_ENDORIBONUCLEASE_ACTIVITY_PRODUCING_3_PHOSPHOMONOESTERS	1.723298	0.9815504	0.0002046	0.0527555	0.5188481	3	RNASE2/RNASE1/TSEN34
GOMF_CARBOXYLIC_ACID_BINDING	2.066286	0.6265775	0.0002909	0.0617214	0.4984931	30	S100A8/S100A9/PTGDS/SLC19A1/SCP2/…
GOMF_SEQUENCE_SPECIFIC_MRNA_BINDING	-1.674101	-0.9327355	0.0003886	0.0721290	0.4984931	5	DHX9/ETF1

11.3.3.3.3.6 cluster_7

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOMF_ANTIGEN_BINDING	2.206274	0.7773854	0.0e+00	0.0000062	0.7614608	65	JCHAIN/IGHA1/IGKC/TRAV8-4/TRBV12-3/…
GOMF_IMMUNOGLOBULIN_RECEPTOR_BINDING	2.061595	0.8908119	3.8e-06	0.0030738	0.6105269	17	JCHAIN/IGHA1/IGKC/IGLL5/IGHA2/…

11.3.3.4 GO_CC

11.3.3.4.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.4.2 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.4.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Covid_Mild for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Covid_Mild, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.3.3.4.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_CHROMOSOMAL_REGION	2.153562	0.6019238	0.0000000	0.0000000	1.0073180	294	CENPM/MCM4/CENPN/MCM2/MCM7/…
GOCC_T_CELL_RECEPTOR_COMPLEX	2.463714	0.8062666	0.0000000	0.0000000	0.9653278	73	TRBV7-9/TRBV28/CD8B/TRAV8-4/TRBV30/…
GOCC_PLASMA_MEMBRANE_SIGNALING_RECEPTOR_COMPLEX	2.345398	0.7105977	0.0000000	0.0000000	0.9436322	132	TRBV7-9/TRBV28/CD8B/TRAV8-4/TRBV30/…
GOCC_RECEPTOR_COMPLEX	2.190003	0.6324329	0.0000000	0.0000000	0.8986712	201	TRBV7-9/TRBV28/CD8B/TRAV8-4/TRBV30/…
GOCC_PLASMA_MEMBRANE_PROTEIN_COMPLEX	2.042605	0.5792467	0.0000000	0.0000000	0.8634154	266	TRBV7-9/TRBV28/CD8B/TRAV8-4/TRBV30/…
GOCC_NUCLEAR_CHROMOSOME	2.121877	0.6186505	0.0000000	0.0000000	0.8140358	180	H3C2/GINS2/MCM4/MCM2/MCM7/…
GOCC_PROTEIN_DNA_COMPLEX	2.184620	0.6481419	0.0000000	0.0000000	0.8140358	150	H3C2/GINS2/MCM2/CDC45/H2AX/…
GOCC_CONDENSED_CHROMOSOME	2.074924	0.5995676	0.0000000	0.0000000	0.8140358	199	CENPM/CENPN/MKI67/CENPU/ZWINT/…
GOCC_DNA_PACKAGING_COMPLEX	2.242180	0.7117612	0.0000000	0.0000001	0.7881868	92	H3C2/NCAPH/SMC4/H2BC11/H2AZ1/…
GOCC_CONDENSED_CHROMOSOME_CENTROMERIC_REGION	2.000353	0.5969363	0.0000003	0.0000236	0.6749629	145	CENPM/CENPN/CENPU/ZWINT/CENPW/…
GOCC_DENDRITE_MEMBRANE	-1.673108	-0.9045261	0.0045097	0.0987124	0.4070179	5	ATP2B1/AKAP5
GOCC_CHLORIDE_CHANNEL_COMPLEX	-1.725759	-0.8015903	0.0031206	0.0749714	0.4317077	9	OSTM1/TTYH2/CLCC1
GOCC_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT	-1.837411	-0.5694257	0.0023245	0.0620873	0.4317077	50	RPL6/RPL36A/RPL10A/RPL5/RPL36/…
GOCC_POLYSOMAL_RIBOSOME	-1.838438	-0.6344751	0.0023322	0.0620873	0.4317077	29	RPL6/RPL36A/RPL10A/RPL36/RPS23/…
GOCC_INTEGRAL_COMPONENT_OF_LYSOSOMAL_MEMBRANE	-1.657898	-0.9489220	0.0013453	0.0456942	0.4550599	4	SLC46A3
GOCC_CYTOSOLIC_RIBOSOME	-1.697702	-0.4786446	0.0011482	0.0403915	0.4550599	91	RPL6/RPL36A/RPL10A/RPL5/RPL36/…
GOCC_RIBOSOME	-1.552780	-0.3895046	0.0008887	0.0336693	0.4772708	194	MPV17L2/MRPL58/RPL6/MRPL42/RPL36A/…
GOCC_POLYSOME	-1.865676	-0.5621103	0.0008361	0.0329405	0.4772708	58	RPL6/DIS3L2/RPL36A/RPL10A/RPL36/…
GOCC_INTRINSIC_COMPONENT_OF_VACUOLAR_MEMBRANE	-1.838011	-0.8537298	0.0005833	0.0249817	0.4772708	9	SLC46A3

11.3.3.4.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_IMMUNOGLOBULIN_COMPLEX	2.728632	0.7431779	0.0000000	0.0000000	1.2378967	122	IGHV5-10-1/IGHV3-49/IGKV6-21/IGLV4-3/IGLV5-37/…
GOCC_IMMUNOGLOBULIN_COMPLEX_CIRCULATING	2.492987	0.7658562	0.0000000	0.0000000	0.9325952	60	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOCC_EXTERNAL_SIDE_OF_PLASMA_MEMBRANE	1.770614	0.4556816	0.0000053	0.0010405	0.6105269	204	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOCC_CELL_SURFACE	1.637783	0.4000528	0.0000051	0.0010405	0.6105269	347	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOCC_SIDE_OF_MEMBRANE	1.517654	0.3731209	0.0001565	0.0140818	0.5188481	329	IGHV5-10-1/IGHV3-49/IGHV3-66/IGHV1-69-2/IGHV6-1/…
GOCC_RIBOSOME	-1.695147	-0.4193540	0.0001520	0.0140818	0.5188481	190	RPL30/MRPL1/MRPL46/MRPL15/RPS5/…
GOCC_RIBONUCLEOPROTEIN_COMPLEX	-1.477137	-0.3269321	0.0000212	0.0023274	0.5756103	594	RPL30/MRPL1/EFTUD2/EBNA1BP2/USP39/…
GOCC_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT	-2.140987	-0.6615629	0.0000129	0.0016015	0.5933255	49	MRPL1/RPL22/RPL17/RPL13/RPL29/…
GOCC_RIBOSOMAL_SUBUNIT	-1.875132	-0.4750649	0.0000103	0.0014601	0.5933255	160	RPL30/MRPL1/MRPL46/MRPL15/RPS5/…
GOCC_LARGE_RIBOSOMAL_SUBUNIT	-2.088079	-0.5647408	0.0000098	0.0014601	0.5933255	103	RPL30/MRPL1/MRPL46/MRPL15/RPL22/…
GOCC_CYTOSOLIC_RIBOSOME	-2.118784	-0.5902958	0.0000023	0.0007649	0.6272567	89	RPL30/MRPL1/RPS5/RPL22/RPL35/…

11.3.3.4.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_T_CELL_RECEPTOR_COMPLEX	1.903663	0.5366106	0.0000400	0.0195423	0.5573322	94	TRBV18/TRBV10-1/TRAV25/TRBV11-1/TRAV5/…
GOCC_PLASMA_MEMBRANE_SIGNALING_RECEPTOR_COMPLEX	1.767999	0.4695008	0.0000340	0.0195423	0.5573322	150	TRBV18/TRAV27/TRBV10-1/TRAV25/TRBV11-1/…
GOCC_IMMUNOGLOBULIN_COMPLEX	1.885940	0.5464453	0.0001772	0.0577721	0.5188481	77	IGKV2-30/IGHV3-49/IGLV2-23/IGLV4-69/IGKV1-16/…
GOCC_RECEPTOR_COMPLEX	1.606175	0.4050069	0.0002639	0.0645330	0.4984931	218	TRBV18/TRAV27/TRBV10-1/TRAV25/TRBV11-1/…
GOCC_PLASMA_MEMBRANE_PROTEIN_COMPLEX	1.555189	0.3821801	0.0004614	0.0902469	0.4984931	279	TRBV18/TRAV27/HLA-DQB1/TRBV10-1/TRAV25/…

11.3.3.4.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_SIDE_OF_MEMBRANE	1.846697	0.4466567	0.0000000	0.0000207	0.7337620	308	CXCL10/CX3CR1/FCGR2B/FCGR3A/CXCL9/…
GOCC_EXTERNAL_SIDE_OF_PLASMA_MEMBRANE	1.909978	0.4893222	0.0000002	0.0000913	0.6901325	192	CXCL10/CX3CR1/ITGA4/FCGR2B/CD86/…
GOCC_MHC_CLASS_II_PROTEIN_COMPLEX	2.007781	0.8348291	0.0000609	0.0100325	0.5573322	14	HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-DQB1/HLA-DQA1/…
GOCC_ENDOPLASMIC_RETICULUM_CHAPERONE_COMPLEX	1.948849	0.8805825	0.0000531	0.0100325	0.5573322	10	SDF2L1/DNAJB11/P4HB/DNAJC10/PDIA6/…
GOCC_COLLAGEN_CONTAINING_EXTRACELLULAR_MATRIX	1.822406	0.5069833	0.0000814	0.0100595	0.5384341	98	S100A8/C1QA/C1QB/S100A9/C1QC/…
GOCC_MHC_PROTEIN_COMPLEX	1.930571	0.7173907	0.0003378	0.0303714	0.4984931	22	HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-G/HLA-DQB1/…
GOCC_ENDOCYTIC_VESICLE_LUMEN	1.895734	0.8172113	0.0003854	0.0317595	0.4984931	12	HP/CALR/APOA1/HYOU1/CTSL/…
GOCC_CLATHRIN_COATED_ENDOCYTIC_VESICLE_MEMBRANE	1.868321	0.5999068	0.0004537	0.0345193	0.4984931	41	HLA-DRB1/CD9/HLA-DPA1/HLA-DQB1/HLA-DQA1/…
GOCC_PROTEIN_LIPID_COMPLEX	1.926354	0.7810465	0.0005202	0.0367516	0.4772708	16	DBI/APOA1/MSR1/BIN1/APOO/…
GOCC_LUMENAL_SIDE_OF_ENDOPLASMIC_RETICULUM_MEMBRANE	1.812523	0.6635407	0.0015532	0.0808476	0.4550599	25	HLA-DRB1/HLA-DPA1/HLA-G/HLA-DQB1/HLA-DQA1/…
GOCC_CYTOSOLIC_RIBOSOME	-1.737803	-0.4781041	0.0007586	0.0494222	0.4772708	89	RPL23/RPL18A/RPL29/RPL18/RPL36AL/…
GOCC_CYTOSOLIC_LARGE_RIBOSOMAL_SUBUNIT	-1.844486	-0.5587320	0.0008101	0.0494222	0.4772708	50	RPL23/RPL18A/RPL29/RPL18/RPL36AL/…

11.3.3.4.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_MHC_CLASS_II_PROTEIN_COMPLEX	2.370310	0.8615979	0.0000035	0.0019067	0.6272567	15	HLA-DQA1/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DQB1/…
GOCC_CLATHRIN_COATED_ENDOCYTIC_VESICLE_MEMBRANE	2.341537	0.7377665	0.0000041	0.0019067	0.6105269	26	HLA-DQA1/FCGR1A/HLA-DRB1/HLA-DPB1/HLA-DPA1/…
GOCC_CLATHRIN_COATED_ENDOCYTIC_VESICLE	2.245486	0.6889069	0.0000300	0.0069595	0.5756103	30	HLA-DQA1/CLTA/FCGR1A/HLA-DRB1/HLA-DPB1/…
GOCC_CLATHRIN_COATED_VESICLE_MEMBRANE	2.007773	0.5721868	0.0002714	0.0493861	0.4984931	38	HLA-DQA1/CLTA/FCGR1A/HLA-DRB1/HLA-DPB1/…
GOCC_MHC_PROTEIN_COMPLEX	2.063584	0.6905143	0.0005325	0.0585630	0.4772708	20	HLA-DQA1/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DQB1/…
GOCC_EXTERNAL_ENCAPSULATING_STRUCTURE	1.756124	0.4522910	0.0006322	0.0585630	0.4772708	64	S100A8/S100A9/CLU/VCAN/CTSH/…
GOCC_ANCHORED_COMPONENT_OF_PLASMA_MEMBRANE	1.704845	0.9721144	0.0008103	0.0626606	0.4772708	3	CD14/CD2
GOCC_CLATHRIN_COATED_VESICLE	1.691293	0.4355936	0.0015421	0.0954059	0.4550599	64	HLA-DQA1/CLTA/FCGR1A/HLA-DRB1/HLA-DPB1/…
GOCC_INTRACELLULAR_PROTEIN_CONTAINING_COMPLEX	-1.394951	-0.4214633	0.0010681	0.0729824	0.4550599	299	TMEM183A/UBE2D2/PCGF5/PSMD4/PAXX/…
GOCC_EXTRINSIC_COMPONENT_OF_ENDOPLASMIC_RETICULUM_MEMBRANE	-1.569832	-0.9305318	0.0011010	0.0729824	0.4550599	4	TOR1A/PML/CCDC115
GOCC_COP9_SIGNALOSOME	-1.719149	-0.7984972	0.0006942	0.0585630	0.4772708	11	COPS9/COPS7A/COPS2/COPS3/COPS5
GOCC_U5_SNRNP	-1.776350	-0.7818248	0.0003193	0.0493861	0.4984931	14	SNRPD1/SNRNP40/SNRPG/SNRPN/SNRPD3
GOCC_CATALYTIC_COMPLEX	-1.373552	-0.4039981	0.0000211	0.0065218	0.5756103	659	TMEM183A/SNRPD1/UBE2D2/PCGF5/SNRNP40/…

11.3.3.4.3.6 cluster_7

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
GOCC_T_CELL_RECEPTOR_COMPLEX	2.428293	0.8660192	0.00e+00	0.0000000	0.9759947	55	TRBV24-1/TRBV7-6/TRBV7-2/TRAV3/TRAV5/…
GOCC_PLASMA_MEMBRANE_PROTEIN_COMPLEX	2.024855	0.6560111	0.00e+00	0.0000000	0.9759947	205	TRBV24-1/TRBV7-6/TRBV7-2/TRAV3/TRAV5/…
GOCC_PLASMA_MEMBRANE_SIGNALING_RECEPTOR_COMPLEX	2.224667	0.7555429	0.00e+00	0.0000000	0.9325952	97	TRBV24-1/TRBV7-6/TRBV7-2/TRAV3/TRAV20/…
GOCC_RECEPTOR_COMPLEX	2.140080	0.7094074	0.00e+00	0.0000000	0.9325952	135	TRBV24-1/TRBV7-6/TRBV7-2/TRAV3/TRAV5/…
GOCC_IMMUNOGLOBULIN_COMPLEX	2.270549	0.8803607	0.00e+00	0.0000019	0.7477397	29	IGKV1-9/JCHAIN/IGHA1/IGKC/IGKV1-6/…
GOCC_IMMUNOGLOBULIN_COMPLEX_CIRCULATING	2.116352	0.9200841	2.00e-07	0.0000295	0.6901325	15	JCHAIN/IGHA1/IGKC/IGLL5/IGHA2/…
GOCC_IGA_IMMUNOGLOBULIN_COMPLEX	1.673127	0.9869494	5.00e-06	0.0006066	0.6105269	4	JCHAIN/IGHA1/IGHA2/IGKV3-20
GOCC_EXTERNAL_SIDE_OF_PLASMA_MEMBRANE	1.764678	0.5820632	5.70e-06	0.0006079	0.6105269	147	IGHA1/IGKC/CX3CR1/FCRL6/TRGV5/…
GOCC_BLOOD_MICROPARTICLE	2.035826	0.7727473	1.06e-05	0.0010199	0.5933255	34	JCHAIN/IGHA1/IGKC/IGHA2/IGKV3-20/…
GOCC_CELL_SURFACE	1.546065	0.4966569	3.60e-05	0.0031596	0.5573322	259	IGHA1/IGKC/TYROBP/CX3CR1/FCRL6/…

11.3.3.5 CP_REACTOME

11.3.3.5.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.5.2 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.5.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Covid_Mild for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Covid_Mild, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.3.3.5.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_CELL_CYCLE	2.336511	0.6167624	0.0000000	0.0000000	1.5092628	606	TYMS/H3C2/GINS2/CENPM/MCM4/…
REACTOME_CELL_CYCLE_MITOTIC	2.416861	0.6461838	0.0000000	0.0000000	1.4675240	476	TYMS/H3C2/GINS2/CENPM/MCM4/…
REACTOME_DNA_REPLICATION	2.426995	0.7126979	0.0000000	0.0000000	1.0864405	165	H3C2/GINS2/MCM4/MCM2/MCM7/…
REACTOME_CELL_CYCLE_CHECKPOINTS	2.321792	0.6551597	0.0000000	0.0000000	1.0864405	239	CENPM/MCM4/CENPN/MCM2/CLSPN/…
REACTOME_DNA_REPLICATION_PRE_INITIATION	2.480731	0.7554312	0.0000000	0.0000000	1.0574636	125	H3C2/MCM4/MCM2/MCM7/CDC45/…
REACTOME_MITOTIC_G1_PHASE_AND_G1_S_TRANSITION	2.452254	0.7503150	0.0000000	0.0000000	1.0574636	122	TYMS/MCM4/TK1/RRM2/MCM2/…
REACTOME_RESOLUTION_OF_SISTER_CHROMATID_COHESION	2.257934	0.6973454	0.0000000	0.0000000	0.8513391	110	CENPM/CENPN/CENPU/ZWINT/CDC20/…
REACTOME_ACTIVATION_OF_ATR_IN_RESPONSE_TO_REPLICATION_STRESS	2.305606	0.8866198	0.0000000	0.0000000	0.8140358	29	MCM4/MCM2/CLSPN/MCM7/CDC45/…
REACTOME_DEPOSITION_OF_NEW_CENPA_CONTAINING_NUCLEOSOMES_AT_THE_CENTROMERE	2.278048	0.7832954	0.0000000	0.0000000	0.8140358	56	CENPM/CENPN/CENPU/CENPW/H2AX/…
REACTOME_MEIOTIC_RECOMBINATION	2.294077	0.7767556	0.0000000	0.0000001	0.8012156	63	H3C2/RAD51/H2AX/RBBP8/H2AC14/…
REACTOME_ROS_AND_RNS_PRODUCTION_IN_PHAGOCYTES	-1.625722	-0.5977521	0.0080684	0.0851781	0.3807304	25	ATP6V1E2/NCF4/ATP6V0C/ATP6V1E1/NOS3/…
REACTOME_SUMOYLATION_OF_SUMOYLATION_PROTEINS	-1.639650	-0.5492923	0.0060783	0.0673323	0.4070179	35	NUP188/NUP88/NUP50/NUP35/NUP205/…
REACTOME_RESPONSE_OF_EIF2AK4_GCN2_TO_AMINO_ACID_DEFICIENCY	-1.539416	-0.4243047	0.0047827	0.0549138	0.4070179	99	RPL6/RPL36A/RPL10A/RPL5/RPL36/…
REACTOME_EUKARYOTIC_TRANSLATION_INITIATION	-1.537413	-0.4301516	0.0043859	0.0518722	0.4070179	94	RPL6/RPL36A/RPL10A/RPL5/RPL36/…
REACTOME_BETA_OXIDATION_OF_VERY_LONG_CHAIN_FATTY_ACIDS	-1.726972	-0.7958374	0.0027579	0.0349849	0.4317077	10	ECI2/DECR2/EHHADH/ACOX1
REACTOME_ROLE_OF_ABL_IN_ROBO_SLIT_SIGNALING	-1.612813	-0.9462357	0.0021098	0.0286094	0.4317077	4	CAP1/CLASP2
REACTOME_SLC_TRANSPORTER_DISORDERS	-1.823149	-0.5554837	0.0011372	0.0165625	0.4550599	53	SLC9A6/NUP188/NUP88/SLC4A4/SLC22A18/…
REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION	-1.777327	-0.5042382	0.0004321	0.0073884	0.4984931	86	RPL6/RPL36A/RPL10A/EEF1G/RPL5/…
REACTOME_RRNA_PROCESSING	-1.634518	-0.4149927	0.0001770	0.0033145	0.5188481	191	RPL6/TBL3/ISG20L2/RPL36A/RPL10A/…
REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE	-1.790883	-0.4936929	0.0001126	0.0022410	0.5384341	107	SRP9/RPL6/TRAM1/RPL36A/RPL10A/…

11.3.3.5.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_COMPLEMENT_CASCADE	2.009910	0.6029164	0.0000050	0.0025781	0.6105269	66	IGHV1-46/IGKV1D-16/IGLV2-23/IGKV3D-20/CFD/…
REACTOME_ANTIGEN_ACTIVATES_B_CELL_RECEPTOR_BCR_LEADING_TO_GENERATION_OF_SECOND_MESSENGERS	1.975443	0.5805936	0.0000048	0.0025781	0.6105269	78	IGHV1-46/IGKV1D-16/IGLV2-23/IGKV3D-20/IGHV4-34/…
REACTOME_CD22_MEDIATED_BCR_REGULATION	1.979083	0.6131349	0.0000169	0.0030933	0.5756103	57	IGHV1-46/IGKV1D-16/IGLV2-23/IGKV3D-20/IGHV4-34/…
REACTOME_ROLE_OF_LAT2_NTAL_LAB_ON_CALCIUM_MOBILIZATION	1.973968	0.6000759	0.0000171	0.0030933	0.5756103	62	IGHV1-46/IGKV1D-16/IGLV2-23/IGKV3D-20/IGHV4-34/…
REACTOME_FCERI_MEDIATED_CA_2_MOBILIZATION	1.964366	0.5798315	0.0000106	0.0030933	0.5933255	76	IGHV1-46/IGKV1D-16/IGLV2-23/IGKV3D-20/IGHV4-34/…
REACTOME_FCERI_MEDIATED_MAPK_ACTIVATION	1.940688	0.5775116	0.0000129	0.0030933	0.5933255	72	IGHV1-46/IGKV1D-16/JUN/IGLV2-23/IGKV3D-20/…
REACTOME_ROLE_OF_PHOSPHOLIPIDS_IN_PHAGOCYTOSIS	1.944950	0.5805449	0.0000230	0.0032541	0.5756103	71	IGHV1-46/IGKV1D-16/IGLV2-23/IGKV3D-20/IGHV4-34/…
REACTOME_INITIAL_TRIGGERING_OF_COMPLEMENT	1.954073	0.6053867	0.0000281	0.0036550	0.5756103	57	IGHV1-46/IGKV1D-16/IGLV2-23/IGKV3D-20/CFD/…
REACTOME_CREATION_OF_C4_AND_C2_ACTIVATORS	1.943205	0.6062028	0.0000476	0.0049445	0.5573322	54	IGHV1-46/IGKV1D-16/IGLV2-23/IGKV3D-20/IGHV4-34/…
REACTOME_FCGR_ACTIVATION	1.899748	0.5826955	0.0000732	0.0060082	0.5384341	60	IGHV1-46/IGKV1D-16/IGLV2-23/IGKV3D-20/IGHV4-34/…
REACTOME_REGULATION_BY_C_FLIP	-1.904890	-0.8620732	0.0009415	0.0432820	0.4772708	9	TNFSF10
REACTOME_CASPASE_ACTIVATION_VIA_DEATH_RECEPTORS_IN_THE_PRESENCE_OF_LIGAND	-1.910440	-0.7941829	0.0009439	0.0432820	0.4772708	12	TNFSF10
REACTOME_RESPONSE_OF_EIF2AK4_GCN2_TO_AMINO_ACID_DEFICIENCY	-1.939184	-0.5161508	0.0001256	0.0093251	0.5188481	99	RPL30/RPS5/RPL22/RPL35/RPL17/…
REACTOME_NONSENSE_MEDIATED_DECAY_NMD	-2.000480	-0.5211696	0.0001032	0.0080426	0.5384341	111	RPL30/RPS5/RPL22/GSPT2/RPL35/…
REACTOME_CELLULAR_RESPONSE_TO_STARVATION	-1.847517	-0.4701529	0.0000642	0.0058851	0.5384341	137	RPL30/NPRL3/FNIP1/RPS5/RPL22/…
REACTOME_INFLUENZA_INFECTION	-1.841868	-0.4609878	0.0000590	0.0057483	0.5573322	150	RPL30/NUP93/RPS5/NUP98/RPL22/…
REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION	-2.078395	-0.5672509	0.0000340	0.0040765	0.5573322	87	RPL30/RPS5/RPL22/RPL35/RPL17/…
REACTOME_RRNA_PROCESSING	-1.857234	-0.4566138	0.0000124	0.0030933	0.5933255	189	RPL30/EBNA1BP2/WDR12/RPP40/TRMT10C/…
REACTOME_SELENOAMINO_ACID_METABOLISM	-2.010487	-0.5373524	0.0000198	0.0030933	0.5756103	98	RPL30/RPS5/RPL22/RPL35/RPL17/…
REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE	-2.025702	-0.5315205	0.0000194	0.0030933	0.5756103	108	RPL30/RPS5/RPN2/RPL22/RPL35/…

11.3.3.5.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM	1.649233	0.3886906	0.0000002	0.0003546	0.6901325	485	IFITM3/HLA-DQB1/SOCS2/PIM1/IFNGR2/…
REACTOME_SIGNALING_BY_INTERLEUKINS	1.604933	0.3967749	0.0000201	0.0156992	0.5756103	301	SOCS2/PIM1/FOS/CISH/JUN/…
REACTOME_GROWTH_HORMONE_RECEPTOR_SIGNALING	2.009447	0.7962464	0.0000495	0.0258632	0.5573322	17	SOCS2/CISH/SOCS3/SOCS1/STAT1/…
REACTOME_INTERLEUKIN_7_SIGNALING	1.961555	0.7252117	0.0001129	0.0442082	0.5384341	24	SOCS2/CISH/H3C2/SOCS1/H3C10/…

11.3.3.5.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_MHC_CLASS_II_ANTIGEN_PRESENTATION	1.974478	0.5634924	0.0000079	0.0122063	0.5933255	87	TUBB2A/HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-DQB1/…
REACTOME_GPCR_LIGAND_BINDING	1.912075	0.5410320	0.0000226	0.0122063	0.5756103	96	CXCL10/CX3CR1/CXCL9/PTGER2/FPR2/…
REACTOME_PD_1_SIGNALING	2.020237	0.7966822	0.0001194	0.0358340	0.5384341	16	HLA-DRB1/HLA-DPA1/HLA-DQB1/HLA-DQA1/HLA-DPB1/…
REACTOME_CHEMOKINE_RECEPTORS_BIND_CHEMOKINES	1.995526	0.7619693	0.0001373	0.0358340	0.5188481	20	CXCL10/CX3CR1/CXCL9/CCL3L3/CCL4/…
REACTOME_DEPOSITION_OF_NEW_CENPA_CONTAINING_NUCLEOSOMES_AT_THE_CENTROMERE	1.905490	0.6088303	0.0002177	0.0402353	0.5188481	45	CENPX/H2AZ2/H2AC8/H2BC11/MIS18BP1/…
REACTOME_GENERATION_OF_SECOND_MESSENGER_MOLECULES	1.910266	0.6833438	0.0006296	0.0646308	0.4772708	26	HLA-DRB1/HLA-DPA1/HLA-DQB1/HLA-DQA1/HLA-DPB1/…
REACTOME_APC_C_MEDIATED_DEGRADATION_OF_CELL_CYCLE_PROTEINS	1.684110	0.4931344	0.0006350	0.0646308	0.4772708	76	PTTG1/UBE2C/UBE2D1/PSMB3/PSMD3/…
REACTOME_CHROMOSOME_MAINTENANCE	1.680580	0.4689999	0.0005802	0.0646308	0.4772708	106	CENPX/RFC4/H2AZ2/H2AC8/H2BC11/…
REACTOME_PEPTIDE_LIGAND_BINDING_RECEPTORS	1.809106	0.5990433	0.0010158	0.0757483	0.4550599	40	CXCL10/CX3CR1/CXCL9/FPR2/CCL3L3/…
REACTOME_CLASS_A_1_RHODOPSIN_LIKE_RECEPTORS	1.741688	0.5429726	0.0009274	0.0757483	0.4772708	53	CXCL10/CX3CR1/CXCL9/CCL3L3/CYSLTR1/…
REACTOME_EUKARYOTIC_TRANSLATION_INITIATION	-1.594646	-0.4515595	0.0014797	0.0965472	0.4550599	94	EIF5/RPL23/RPL18A/RPL29/RPL18/…
REACTOME_IL_6_TYPE_CYTOKINE_RECEPTOR_LIGAND_INTERACTIONS	-1.759275	-0.8961184	0.0010663	0.0758989	0.4550599	6	TYK2
REACTOME_HUR_ELAVL1_BINDS_AND_STABILIZES_MRNA	-1.778908	-0.8289656	0.0010054	0.0757483	0.4550599	8	TNFSF13/PRKCD
REACTOME_INTERLEUKIN_6_FAMILY_SIGNALING	-1.893517	-0.7834492	0.0007116	0.0655468	0.4772708	13	TYK2/CBL
REACTOME_NONSENSE_MEDIATED_DECAY_NMD	-1.626813	-0.4475012	0.0006603	0.0646308	0.4772708	110	RPL23/RPL18A/RPL29/RPL36AL/FAU/…
REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE	-1.684179	-0.4664993	0.0002866	0.0408023	0.4984931	106	SRP72/RPL23/RPL18A/RPL29/RPL18/…
REACTOME_RESPONSE_OF_EIF2AK4_GCN2_TO_AMINO_ACID_DEFICIENCY	-1.736423	-0.4873728	0.0002312	0.0402353	0.5188481	98	DDIT3/RPL23/RPL18A/RPL29/RPL18/…
REACTOME_INTERLEUKIN_35_SIGNALLING	-1.874964	-0.8737273	0.0001602	0.0358340	0.5188481	8	TYK2
REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION	-1.827911	-0.5246989	0.0000465	0.0182012	0.5573322	86	EEF1D/RPL23/RPL18A/RPL29/RPL18/…

11.3.3.5.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_PD_1_SIGNALING	2.359489	0.9174443	0.0000003	0.0005014	0.6749629	12	HLA-DQA1/HLA-DRB1/HLA-DPB1/HLA-DPA1/CD4/…
REACTOME_ANTIMICROBIAL_PEPTIDES	2.276290	0.8737225	0.0000049	0.0018192	0.6105269	13	S100A8/S100A9/CLU/CD4
REACTOME_TOLL_LIKE_RECEPTOR_TLR1_TLR2_CASCADE	2.171612	0.6254880	0.0000041	0.0018192	0.6105269	44	S100A8/CD14/S100A9/S100A12/MEF2A/…
REACTOME_DISEASES_OF_IMMUNE_SYSTEM	2.237486	0.8398287	0.0000126	0.0037327	0.5933255	14	S100A8/CD14/S100A9/NFKB1
REACTOME_IRAK4_DEFICIENCY_TLR2_4	2.217217	0.8918020	0.0000153	0.0037688	0.5756103	11	S100A8/CD14/S100A9
REACTOME_REGULATION_OF_TLR_BY_ENDOGENOUS_LIGAND	2.164041	0.9115141	0.0000281	0.0059318	0.5756103	9	S100A8/CD14/S100A9/GSDMD
REACTOME_GENERATION_OF_SECOND_MESSENGER_MOLECULES	2.107443	0.7523408	0.0002823	0.0479045	0.4984931	17	HLA-DQA1/HLA-DRB1/HLA-DPB1/HLA-DPA1/CD4/…
REACTOME_TOLL_LIKE_RECEPTOR_CASCADES	1.835710	0.4712314	0.0006201	0.0834250	0.4772708	65	S100A8/CD14/S100A9/S100A12/MEF2A/…
REACTOME_TRANSLATION	-1.443926	-0.4391890	0.0004220	0.0624586	0.4984931	236	RPL22L1/AURKAIP1/EIF3A/YARS1/EIF2S3/…
REACTOME_MRNA_SPLICING	-1.533618	-0.4797735	0.0002913	0.0479045	0.4984931	153	SNRPD1/PUF60/SNRNP40/SNRPG/CASC3/…

11.3.3.5.3.6 cluster_7

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
REACTOME_FCERI_MEDIATED_MAPK_ACTIVATION	2.109754	0.8243360	0.0000097	0.0099276	0.5933255	28	IGHV1-2/FOS/FCER1G/IGKV3-20/IGKV4-1/…
REACTOME_IMMUNOREGULATORY_INTERACTIONS_BETWEEN_A_LYMPHOID_AND_A_NON_LYMPHOID_CELL	1.931184	0.6760622	0.0000130	0.0099276	0.5933255	70	IGHV1-2/TRAV8-4/TYROBP/TRBV12-3/KLRG1/…
REACTOME_CD22_MEDIATED_BCR_REGULATION	1.974327	0.8717231	0.0000394	0.0129559	0.5573322	14	IGHV1-2/CD79B/CD79A/IGKV3-20/IGHM/…
REACTOME_CELL_SURFACE_INTERACTIONS_AT_THE_VASCULAR_WALL	1.953504	0.6885833	0.0000310	0.0129559	0.5573322	65	IGHV1-2/JCHAIN/IGHA1/FCER1G/IGHA2/…
REACTOME_SCAVENGING_OF_HEME_FROM_PLASMA	1.937776	0.8842414	0.0000474	0.0129559	0.5573322	12	IGHV1-2/IGHA1/IGHA2/IGKV3-20/IGHV4-59
REACTOME_DNA_DAMAGE_TELOMERE_STRESS_INDUCED_SENESCENCE	1.859313	0.6651506	0.0000508	0.0129559	0.5573322	57	H2BC13/LMNB1/H2BC14/H2BC3/H1-1/…
REACTOME_SENESCENCE_ASSOCIATED_SECRETORY_PHENOTYPE_SASP	1.727124	0.5963017	0.0001164	0.0254469	0.5384341	82	FOS/H2BC13/H2BC14/H2BC3/H2BC11/…
REACTOME_RUNX1_REGULATES_GENES_INVOLVED_IN_MEGAKARYOCYTE_DIFFERENTIATION_AND_PLATELET_FUNCTION	1.780213	0.6322222	0.0002977	0.0569338	0.4984931	61	H2BC13/H2BC14/H2BC3/PRMT1/H2BC11/…
REACTOME_FCGR_ACTIVATION	2.002534	0.8512086	0.0004058	0.0620838	0.4984931	17	IGHV1-2/IGKV3-20/CD247/IGKV4-1/IGHV4-59/…
REACTOME_ACTIVATED_PKN1_STIMULATES_TRANSCRIPTION_OF_AR_ANDROGEN_RECEPTOR_REGULATED_GENES_KLK2_AND_KLK3	1.829496	0.6737836	0.0007073	0.0983737	0.4772708	44	H2BC13/H2BC14/H2BC3/H2BC11/H2AC6/…

11.3.3.6 CP_KEGG

11.3.3.6.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.6.2 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.6.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Covid_Mild for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Covid_Mild, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.3.3.6.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS	2.171408	0.6966576	0.0000000	0.0000007	0.7477397	84	H3C2/H2AX/H2AC14/H2BC11/H2AZ1/…
KEGG_CELL_CYCLE	2.141807	0.6693173	0.0000000	0.0000007	0.7614608	106	MCM2/MCM7/PCNA/CDC45/CHEK1/…
KEGG_DNA_REPLICATION	2.209945	0.8330369	0.0000002	0.0000099	0.6901325	30	MCM4/MCM2/MCM7/PCNA/FEN1/…
KEGG_HEMATOPOIETIC_CELL_LINEAGE	1.985047	0.7252280	0.0000903	0.0041978	0.5384341	37	CD8B/CD3G/IL7R/CD3E/CD3D/…
KEGG_ONE_CARBON_POOL_BY_FOLATE	1.939099	0.8656199	0.0001199	0.0044585	0.5384341	14	TYMS/DHFR/SHMT2/SHMT1/MTHFD2/…
KEGG_PRIMARY_IMMUNODEFICIENCY	1.926642	0.7595443	0.0001469	0.0045542	0.5188481	26	CD8B/IL7R/CD3E/CD3D/UNG/…
KEGG_P53_SIGNALING_PATHWAY	1.799546	0.6276497	0.0004796	0.0127436	0.4984931	53	RRM2/CHEK1/GTSE1/BAX/CCNB2/…
KEGG_OOCYTE_MEIOSIS	1.764657	0.5788121	0.0006797	0.0158039	0.4772708	76	PKMYT1/CDC20/PTTG1/YWHAE/SGO1/…
KEGG_GLYCINE_SERINE_AND_THREONINE_METABOLISM	1.713269	0.7798135	0.0044324	0.0716371	0.4070179	13	SHMT2/PSAT1/PSPH/SHMT1/PHGDH/…
KEGG_GLUTATHIONE_METABOLISM	1.700636	0.6373135	0.0046217	0.0716371	0.4070179	32	GSTM1/GGT7/GSTM2/GSR/RRM1/…
KEGG_RIBOSOME	-1.642708	-0.4668809	0.0013922	0.0287728	0.4550599	84	RPL6/RPL36A/RPL10A/RPL5/RPL36/…

11.3.3.6.3.2 cluster_2

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_RIBOSOME	-2.058845	-0.5795824	1.04e-05	0.0019406	0.5933255	84	RPL30/RPS5/RPL22/RPL35/RPL17/…

11.3.3.6.3.3 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS	1.852556	0.5516434	0.0001120	0.0207161	0.5384341	71	HLA-DQB1/CD40LG/H3C2/H3C10/H3C11/…
KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION	1.767137	0.5097968	0.0002292	0.0212001	0.5188481	90	IFNGR2/CD40LG/TNFRSF18/CCL5/TNFRSF4/…
KEGG_LEISHMANIA_INFECTION	1.777612	0.5759608	0.0010579	0.0652342	0.4550599	44	HLA-DQB1/IFNGR2/FOS/NFKBIA/HLA-DQA1/…
KEGG_JAK_STAT_SIGNALING_PATHWAY	1.705611	0.5024723	0.0015053	0.0696205	0.4550599	75	SOCS2/PIM1/IFNGR2/CISH/SOCS3/…
KEGG_ASTHMA	1.753141	0.7114749	0.0026394	0.0976577	0.4317077	16	HLA-DQB1/CD40LG/HLA-DQA1/IL10/HLA-DRA

11.3.3.6.3.4 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS	2.467692	0.7116934	0.0000000	0.0000000	0.8753251	78	FCGR2B/CD86/C1QA/C1QB/FCGR3A/…
KEGG_HEMATOPOIETIC_CELL_LINEAGE	2.084252	0.6643323	0.0000079	0.0007360	0.5933255	44	ITGA4/HLA-DRB1/CD9/CD37/CD4/…
KEGG_ASTHMA	2.031817	0.8275269	0.0000626	0.0029120	0.5384341	15	HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-DQB1/HLA-DQA1/…
KEGG_GRAFT_VERSUS_HOST_DISEASE	1.983153	0.7145278	0.0001142	0.0042481	0.5384341	25	CD86/HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-G/…
KEGG_AUTOIMMUNE_THYROID_DISEASE	1.938784	0.7348842	0.0001764	0.0054671	0.5188481	21	CD86/HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-G/…
KEGG_ANTIGEN_PROCESSING_AND_PRESENTATION	1.879187	0.5763188	0.0002234	0.0059352	0.5188481	51	HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-G/HLA-DQB1/…
KEGG_TYPE_I_DIABETES_MELLITUS	1.922818	0.6927890	0.0002886	0.0059826	0.4984931	25	CD86/HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-G/…
KEGG_CELL_ADHESION_MOLECULES_CAMS	1.839754	0.5574219	0.0002895	0.0059826	0.4984931	57	ITGA4/CD86/HLA-DRB1/HLA-DMB/HLA-DPA1/…
KEGG_ALLOGRAFT_REJECTION	1.904478	0.6861813	0.0003380	0.0062861	0.4984931	25	CD86/HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-G/…
KEGG_VIRAL_MYOCARDITIS	1.889792	0.6082394	0.0004968	0.0084012	0.4772708	42	CD86/HLA-DRB1/HLA-DMB/HLA-DPA1/HLA-G/…
KEGG_TYPE_II_DIABETES_MELLITUS	-1.621156	-0.6346345	0.0069573	0.0862711	0.4070179	19	HK1/PIK3CG/PRKCD/MAPK1/HK3/…
KEGG_RIBOSOME	-1.811246	-0.5190738	0.0000417	0.0025854	0.5573322	84	RPL23/RPL18A/RPL29/RPL18/RPL36AL/…

11.3.3.6.3.5 cluster_6

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KEGG_INTESTINAL_IMMUNE_NETWORK_FOR_IGA_PRODUCTION	2.213485	0.7466168	0.0000352	0.0032235	0.5573322	21	HLA-DQA1/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DQB1/…
KEGG_LEISHMANIA_INFECTION	2.115934	0.6404779	0.0000277	0.0032235	0.5756103	34	HLA-DQA1/IFNGR2/FCGR1A/HLA-DRB1/HLA-DPB1/…
KEGG_CELL_ADHESION_MOLECULES_CAMS	1.958053	0.5846113	0.0003231	0.0197077	0.4984931	36	HLA-DQA1/VCAN/HLA-DRB1/HLA-DPB1/HLA-DPA1/…
KEGG_ASTHMA	2.001552	0.7615573	0.0013252	0.0441455	0.4550599	13	HLA-DQA1/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DQB1/…
KEGG_ALLOGRAFT_REJECTION	1.993175	0.6867902	0.0018186	0.0441455	0.4550599	19	HLA-DQA1/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DQB1/…
KEGG_HEMATOPOIETIC_CELL_LINEAGE	1.958328	0.6422632	0.0016543	0.0441455	0.4550599	23	CD14/HLA-DRB1/CD4/CD37/HLA-DRA/…
KEGG_GRAFT_VERSUS_HOST_DISEASE	1.941524	0.6672288	0.0013107	0.0441455	0.4550599	20	HLA-DQA1/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DQB1/…
KEGG_TYPE_I_DIABETES_MELLITUS	1.901411	0.6534435	0.0019299	0.0441455	0.4550599	20	HLA-DQA1/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DQB1/…
KEGG_AUTOIMMUNE_THYROID_DISEASE	1.949401	0.6884058	0.0027751	0.0564267	0.4317077	18	HLA-DQA1/HLA-DRB1/HLA-DPB1/HLA-DPA1/HLA-DQB1/…
KEGG_VIBRIO_CHOLERAE_INFECTION	-1.620600	-0.6272288	0.0043339	0.0793103	0.4070179	27	KDELR1/ATP6V1B2/KCNQ1/PDIA4/ATP6V0D1/…

11.3.3.7 TFT_GTRD

11.3.3.7.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.7.2 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.7.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Covid_Mild for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Covid_Mild, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.3.3.7.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HSD17B8_TARGET_GENES	2.577136	0.6971699	0.0000000	0.0000000	1.6719967	447	GINS2/TK1/RRM2/CENPN/MKI67/…
E2F5_TARGET_GENES	1.594857	0.4167934	0.0000000	0.0000046	0.7337620	874	GINS2/TK1/RRM2/MKI67/MCM2/…
PSMB5_TARGET_GENES	1.840508	0.5245759	0.0000006	0.0000991	0.6594444	229	RRM2/STMN1/CKS2/TUBA1B/H2AX/…
SETD7_TARGET_GENES	1.454402	0.3819497	0.0000877	0.0090553	0.5384341	708	TYMS/RRM2/STMN1/DHFR/MCM7/…
PHF21A_TARGET_GENES	1.694348	0.4944367	0.0002203	0.0189432	0.5188481	181	PTGDS/TK1/RRM2/CDCA7/UHRF1/…
LHX3_TARGET_GENES	1.871275	0.7355436	0.0004250	0.0274101	0.4984931	25	RBBP8/TOP2A/H2BC9/H2AC4/F12/…
AEBP2_TARGET_GENES	1.372416	0.3590654	0.0005077	0.0291097	0.4772708	802	TYMS/CENPM/MCM4/TK1/CENPN/…
POU2AF1_TARGET_GENES	1.381042	0.3645280	0.0010371	0.0486511	0.4550599	620	TYMS/CENPN/CDCA7/IGLV4-69/CDC45/…
ZNF563_TARGET_GENES	-1.363622	-0.3213080	0.0016575	0.0712735	0.4550599	353	WDR55/PIH1D1/DGKZ/NUP188/ZNF559/…
SUPT20H_TARGET_GENES	-1.207036	-0.2654213	0.0009148	0.0472019	0.4772708	1078	MPV17L2/CCDC191/ZFAS1/TOR1AIP1/DENR/…
DIDO1_TARGET_GENES	-1.217403	-0.2632020	0.0004196	0.0274101	0.4984931	1306	ARL1/CCDC191/PMPCB/TOR1AIP1/RPL6/…
HOXA1_TARGET_GENES	-1.363433	-0.3021310	0.0000126	0.0016197	0.5933255	864	RANBP3/SLC9A6/ARL1/ACAT1/CCDC191/…

11.3.3.7.3.2 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
NKX2_3_TARGET_GENES	-1.437627	-0.3235113	0.0002802	0.0481984	0.4984931	358	INTS2/TTC33/ACADSB/M6PR/CSKMT/…
NFRKB_TARGET_GENES	-1.250958	-0.2551818	0.0000725	0.0231272	0.5384341	1464	BRD8/MIGA1/RAB7A/AAR2/SRRD/…
ELF2_TARGET_GENES	-1.279697	-0.2621921	0.0000896	0.0231272	0.5384341	1169	TSG101/VPS50/RAB7A/RNF115/AAR2/…

11.3.3.7.3.3 cluster_4

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
HSD17B8_TARGET_GENES	1.471109	0.3539828	0.0004746	0.0796607	0.4984931	341	BIRC5/MKI67/PTTG1/TROAP/CDKN3/…
DIDO1_TARGET_GENES	-1.246076	-0.2675079	0.0010422	0.0796607	0.4550599	1243	WDR77/POP4/MRPL54/SUPT7L/RLF/…
ZNF766_TARGET_GENES	-1.284510	-0.2850324	0.0007870	0.0796607	0.4772708	759	NEAT1/LUCAT1/PA2G4/LYN/TMEM115/…
DLX4_TARGET_GENES	-1.344985	-0.3022557	0.0005310	0.0796607	0.4772708	615	WDR77/SUPT7L/CSNK1G1/PFKM/SF3B5/…
GLI3_TARGET_GENES	-1.345558	-0.3052829	0.0007172	0.0796607	0.4772708	545	SVIP/RLF/R3HDM2/APRT/SMC4/…
RFX7_TARGET_GENES	-1.399458	-0.3289083	0.0010828	0.0796607	0.4550599	363	TYK2/WDR77/NEMF/USP48/U2AF2/…
ELF5_TARGET_GENES	-1.459133	-0.3441153	0.0004178	0.0796607	0.4984931	341	HNRNPF/SVIP/LYN/UBL7/WDR43/…

11.3.3.8 TFT_TFT_Legacy

11.3.3.8.1 Summaryplot: Covid_Critical

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Critical. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.8.2 Summaryplot: Covid_Mild

Here we plot a summary of the gene sets/pathways that are enriched in genes overexpressed in Covid_Mild. If there are more than 30 significant pathways total, we plot the top 5 per cluster with the lowest adjusted P value.

11.3.3.8.3 Per-cluster pathway results

Here we plot the pathways that are significantly enriched in the differentially expressed genes between Covid_Critical and Covid_Mild for each cluster.

For each condition, we display a table of the GSEA results. NES refers to Normalized Enrichment Score, the main effect size for GSEA. Positive NES indicates the pathway is enriched in Covid_Critical relative to Covid_Mild, and vice-versa.

If a cluster is missing, it indicates no pathways were significantly differentially expressed.

11.3.3.8.3.1 cluster_1

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
E2F_Q3	2.224835	0.6517409	0.0000000	0.0000000	0.8753251	165	MCM4/MCM2/STMN1/CDCA7/MCM7/…
E2F1_Q4_01	2.167436	0.6323449	0.0000000	0.0000000	0.8634154	173	MCM4/MCM2/STMN1/CDCA7/MCM7/…
E2F_Q4_01	2.170634	0.6295761	0.0000000	0.0000000	0.8513391	180	MCM4/MCM2/STMN1/CDCA7/MCM7/…
E2F_Q6_01	2.110619	0.6141850	0.0000000	0.0000000	0.8390889	176	MCM4/RRM2/MCM2/STMN1/CDCA7/…
E2F_Q3_01	2.102959	0.6092334	0.0000000	0.0000001	0.8012156	182	MCM4/MCM2/STMN1/CDCA7/MCM7/…
E2F_Q4	2.076081	0.6000867	0.0000000	0.0000001	0.7881868	187	MCM4/RRM2/MCM2/CLSPN/STMN1/…
E2F_03	2.052959	0.5969960	0.0000000	0.0000002	0.7749390	179	MCM4/MCM2/STMN1/CDCA7/MCM7/…
E2F4DP1_01	2.006712	0.5775346	0.0000000	0.0000010	0.7477397	190	MCM4/RRM2/MCM2/CLSPN/STMN1/…
E2F1DP1RB_01	2.057916	0.6109312	0.0000000	0.0000021	0.7195128	145	RRM2/MCM2/CLSPN/STMN1/CDCA7/…
KRCTCNNNNMANAGC_UNKNOWN	2.163679	0.7694695	0.0000008	0.0000312	0.6594444	43	H3C2/UBE2C/H2AC14/H2BC11/H2AC16/…
OCT1_03	-1.484513	-0.4114424	0.0038742	0.0908938	0.4317077	110	TP53BP1/ARL1/RMI1/AAK1/AMD1/…
AAGWWRNYGGC_UNKNOWN	-1.592205	-0.4453427	0.0019950	0.0507073	0.4317077	99	ARL1/TMEM187/PCIF1/MYLIP/TRIM8/…

11.3.3.8.3.2 cluster_3

pathway	NES	ES	pval	padj	log2err	size	leadingEdge
KRCTCNNNNMANAGC_UNKNOWN	2.00142	0.6711829	5.1e-05	0.0311284	0.5573322	36	H1-4/H3C2/H1-2/H3C10/H3C11/…

12 Run info

12.1 Run time

hourspassed <- (proc.time() - timestart)[3]/60/60
names(hourspassed) <- 'Hours'
hourspassed

##     Hours 
## 0.5844919

12.2 Memory usage

rm(list=ls())

finalmem <- gc(verbose = T, full = T)

mb <- sum(finalmem[,ncol(finalmem)])
gb <- mb / 1000
gb <- setNames(gb,'Gb used (approximately)')

gb

## Gb used (approximately) 
##                  3.9599

12.3 Session info

# beepr::beep()

sessionInfo()

## R version 4.3.3 (2024-02-29)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux 8.7 (Ootpa)
## 
## Matrix products: default
## BLAS/LAPACK: /gs/gsfs0/home/aferrena/packages/miniconda3/miniconda3/envs/scdapp/lib/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] grid      parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] doParallel_1.0.17     iterators_1.0.14      fgsea_1.28.0         
##  [4] speckle_0.99.7        circlize_0.4.16       Matrix_1.6-5         
##  [7] ggrepel_0.9.5         ggfittext_0.10.2      ggalluvial_0.12.5    
## [10] hdf5r_1.3.9           msigdbr_7.5.1         edgeR_4.0.16         
## [13] limma_3.58.1          ggridges_0.5.6        ggdendro_0.2.0       
## [16] ComplexHeatmap_2.18.0 glmGamPoi_1.14.3      foreach_1.5.2        
## [19] future_1.33.1         DoubletFinder_2.0.4   scDAPP_1.0.0         
## [22] Seurat_5.0.2          SeuratObject_5.0.1    sp_2.1-3             
## [25] RISC_1.6.0            patchwork_1.2.0       lubridate_1.9.3      
## [28] forcats_1.0.0         stringr_1.5.1         dplyr_1.1.4          
## [31] purrr_1.0.2           readr_2.1.5           tidyr_1.3.1          
## [34] tibble_3.2.1          ggplot2_3.5.0         tidyverse_2.0.0      
## 
## loaded via a namespace (and not attached):
##   [1] RcppAnnoy_0.0.22            splines_4.3.3              
##   [3] later_1.3.2                 bitops_1.0-7               
##   [5] polyclip_1.10-6             fastDummies_1.7.3          
##   [7] lifecycle_1.0.4             globals_0.16.2             
##   [9] lattice_0.22-5              MASS_7.3-60                
##  [11] magrittr_2.0.3              sass_0.4.8                 
##  [13] rmarkdown_2.25              plotly_4.10.4              
##  [15] jquerylib_0.1.4             yaml_2.3.8                 
##  [17] httpuv_1.6.14               sctransform_0.4.1          
##  [19] spam_2.10-0                 askpass_1.2.0              
##  [21] spatstat.sparse_3.0-3       reticulate_1.35.0          
##  [23] cowplot_1.1.3               pbapply_1.7-2              
##  [25] RColorBrewer_1.1-3          abind_1.4-5                
##  [27] zlibbioc_1.48.0             Rtsne_0.17                 
##  [29] GenomicRanges_1.54.1        BiocGenerics_0.48.1        
##  [31] RCurl_1.98-1.14             GenomeInfoDbData_1.2.11    
##  [33] IRanges_2.36.0              S4Vectors_0.40.2           
##  [35] irlba_2.3.5.1               listenv_0.9.1              
##  [37] spatstat.utils_3.0-4        pheatmap_1.0.12            
##  [39] umap_0.2.10.0               goftest_1.2-3              
##  [41] RSpectra_0.16-1             spatstat.random_3.2-3      
##  [43] fitdistrplus_1.1-11         parallelly_1.37.1          
##  [45] DelayedMatrixStats_1.24.0   leiden_0.4.3.1             
##  [47] codetools_0.2-19            DelayedArray_0.28.0        
##  [49] tidyselect_1.2.0            shape_1.4.6.1              
##  [51] farver_2.1.1                viridis_0.6.5              
##  [53] matrixStats_1.2.0           stats4_4.3.3               
##  [55] spatstat.explore_3.2-6      jsonlite_1.8.8             
##  [57] GetoptLong_1.0.5            ellipsis_0.3.2             
##  [59] progressr_0.14.0            survival_3.5-8             
##  [61] Matrix.utils_0.9.8          tools_4.3.3                
##  [63] ica_1.0-3                   Rcpp_1.0.12                
##  [65] glue_1.7.0                  gridExtra_2.3              
##  [67] SparseArray_1.2.4           mgcv_1.9-1                 
##  [69] xfun_0.42                   MatrixGenerics_1.14.0      
##  [71] GenomeInfoDb_1.38.6         withr_3.0.0                
##  [73] fastmap_1.1.1               fansi_1.0.6                
##  [75] openssl_2.1.1               digest_0.6.34              
##  [77] timechange_0.3.0            R6_2.5.1                   
##  [79] mime_0.12                   colorspace_2.1-0           
##  [81] scattermore_1.2             tensor_1.5                 
##  [83] spatstat.data_3.0-4         utf8_1.2.4                 
##  [85] generics_0.1.3              data.table_1.15.2          
##  [87] FNN_1.1.4                   httr_1.4.7                 
##  [89] htmlwidgets_1.6.4           S4Arrays_1.2.0             
##  [91] uwot_0.1.16                 pkgconfig_2.0.3            
##  [93] gtable_0.3.4                lmtest_0.9-40              
##  [95] SingleCellExperiment_1.24.0 XVector_0.42.0             
##  [97] htmltools_0.5.7             dotCall64_1.1-1            
##  [99] clue_0.3-65                 scales_1.3.0               
## [101] Biobase_2.62.0              png_0.1-8                  
## [103] knitr_1.45                  tzdb_0.4.0                 
## [105] reshape2_1.4.4              rjson_0.2.21               
## [107] nlme_3.1-164                cachem_1.0.8               
## [109] zoo_1.8-12                  GlobalOptions_0.1.2        
## [111] KernSmooth_2.23-22          miniUI_0.1.1.1             
## [113] pillar_1.9.0                vctrs_0.6.5                
## [115] RANN_2.6.1                  promises_1.2.1             
## [117] xtable_1.8-4                cluster_2.1.6              
## [119] evaluate_0.23               locfit_1.5-9.8             
## [121] cli_3.6.2                   compiler_4.3.3             
## [123] rlang_1.1.3                 crayon_1.5.2               
## [125] grr_0.9.5                   future.apply_1.11.1        
## [127] labeling_0.4.3              densityClust_0.3.3         
## [129] plyr_1.8.9                  stringi_1.8.3              
## [131] BiocParallel_1.36.0         viridisLite_0.4.2          
## [133] deldir_2.0-4                babelgene_22.9             
## [135] munsell_0.5.0               lazyeval_0.2.2             
## [137] spatstat.geom_3.2-9         RcppHNSW_0.6.0             
## [139] hms_1.1.3                   sparseMatrixStats_1.14.0   
## [141] bit64_4.0.5                 statmod_1.5.0              
## [143] shiny_1.8.0                 highr_0.10                 
## [145] SummarizedExperiment_1.32.0 ROCR_1.0-11                
## [147] igraph_1.6.0                bslib_0.6.1                
## [149] fastmatch_1.1-4             bit_4.0.5

Cluster	Healthy_1	Healthy_2	Covid_Mild_1	Covid_Mild_2	Covid_Critical_1	Covid_Critical_2
1	254	196	236	282	83	201
2	199	187	193	98	191	193
3	132	183	155	102	186	158
4	111	141	148	170	180	162
5	47	17	12	8	0	3
6	7	17	9	6	2	2
7	0	1	3	1	7	25

Cluster	Healthy_1	Healthy_2	Covid_Mild_1	Covid_Mild_2	Covid_Critical_1	Covid_Critical_2
1	254	196	236	282	83	201
2	199	187	193	98	191	193
3	132	183	155	102	186	158
4	111	141	148	170	180	162
5	47	17	12	8	0	3
6	7	17	9	6	2	2
7	0	1	3	1	7	25

10X analysis - clustering and integration

Alexander Ferrena, Deyou Zheng

2024-03-05

1 Introduction

1.1 Description of methods

1.2 Pipeline Parameters

1.2.1 Sample names and conditions

1.2.2 Comparisons to perform

1.2.3 Analysis parameters

2 Individual sample processing and analysis

3 Data Quality Assessment

3.1 Filter out poor quality cells

3.1.1 UMI distribution and filtering

3.1.1.1 UMI pre-filter distribution

3.1.1.2 UMI post-filter distribution

3.1.2 Unique feature (gene) distribution and filtering

3.1.2.1 Unique Feature pre-filter distribution

3.1.2.2 Unique Feature post-filter distribution

3.1.3 Mitochondrial content level filtering

3.1.3.1 Mito pre-filter distribution

3.1.3.2 Mito post-filter distribution

3.1.4 Hemoglobin content level filtering

3.1.4.1 Hemoglobin pre-filter distribution

3.1.4.2 Hemoglobin post-filter distribution

4 Processing and analysis for each sample

4.1 Select number of Principal Components (PCs) to use based on elbow plot

4.2 Clustering

4.3 Label transfer from reference scRNA-seq data

5 Individual sample clustering summary plots (pre-integration)

5.1 Sample Healthy_1

5.1.1 UMAP of clusters

5.1.2 Heatmap of cluster markers

5.1.3 QC plots

5.2 Sample Healthy_2

5.2.1 UMAP of clusters

5.2.2 Heatmap of cluster markers

5.2.3 QC plots

5.3 Sample Covid_Mild_1

5.3.1 UMAP of clusters

5.3.2 Heatmap of cluster markers

5.3.3 QC plots

5.4 Sample Covid_Mild_2

5.4.1 UMAP of clusters

5.4.2 Heatmap of cluster markers

5.4.3 QC plots

5.5 Sample Covid_Critical_1

5.5.1 UMAP of clusters

5.5.2 Heatmap of cluster markers

5.5.3 QC plots

5.6 Sample Covid_Critical_2

5.6.1 UMAP of clusters

5.6.2 Heatmap of cluster markers

5.6.3 QC plots

6 Individual sample label transfer cell type predictions

6.1 Sample Healthy_1

6.1.1 Heatmap of label transfer cell type scores

6.1.2 Dotplot of label transfer cell type scores

6.1.3 Heatmap of reference cell type markers

6.1.4 Heatmap of reference cell type markers averaged across clusters

6.1.5 UMAP of top scoring celltypes

6.1.6 UMAP of label transfer scores

6.1.7 Alluvial plot mapping between Louvain clusters to top celltypes

6.1.8 Table showing cluster-celltype mapping

6.1.9 UMAP of cluster-celltype mapping

6.1.10 Violin plots for each cell type

6.1.10.1 MNP

6.1.10.2 B

6.1.10.3 NK

6.1.10.4 T

6.2 Sample Healthy_2

6.2.1 Heatmap of label transfer cell type scores

6.2.2 Dotplot of label transfer cell type scores

6.2.3 Heatmap of reference cell type markers

6.2.4 Heatmap of reference cell type markers averaged across clusters

6.2.5 UMAP of top scoring celltypes

6.2.6 UMAP of label transfer scores

6.2.7 Alluvial plot mapping between Louvain clusters to top celltypes

6.2.8 Table showing cluster-celltype mapping

6.2.9 UMAP of cluster-celltype mapping

6.2.10 Violin plots for each cell type

Cluster	Healthy_1	Healthy_2	Covid_Mild_1	Covid_Mild_2	Covid_Critical_1	Covid_Critical_2
1	254	196	236	282	83	201
2	199	187	193	98	191	193
3	132	183	155	102	186	158
4	111	141	148	170	180	162
5	47	17	12	8	0	3
6	7	17	9	6	2	2
7	0	1	3	1	7	25